PREDICTING INDONESIAN CURRENCY CRISES USING EARLY … · The 1997/98 Asian Financial Crisis was the...

PREDICTING INDONESIAN

CURRENCY CRISES USING EARLY

WARNING SYSTEM MODELS

by

Syaifullah

This thesis is presented for the degree of Doctoral of Philosophy

at The University of Western Australia

Economics Discipline

Business School

The University of Western Australia

2012

i

ABSTRACT

Following the collapse of the Bretton Woods system of exchange rate

management in the 1970s, the frequency of financial crises as well as the

number of countries involved tends to increase. Even today, financial crises are

still a major threat to many economies in the world and will undoubtedly

continue in the future. Indonesia is no exception. With an open economy,

Indonesia has experienced several financial crises. The 1997/98 Asian Financial

Crisis was the worst in recent decades. It affected not only Indonesian macro

economy but also the country’s social and political aspects. As a result this crisis

is known as a multi-dimension crisis.

The enormous impacts and huge recovery cost of financial crises encourage

policy makers and economists to find ways to prevent these crises. This study

aims to make a contribution in this field by constructing models to predict

financial crises, in particular for Indonesia. It adopts and extends the signal

model proposed by Kaminsky et al. (1998) as well as the discrete choice model

proposed by Eichengreen et al. (1996) and Frankel and Rose (1996). In addition,

as an alternative method, this study also applies the artificial neural network

(ANN) model.

The empirical findings indicate that these models perform well in predicting the

Indonesian currency crises within the 24-month crisis window; however, the

ANN model outperforms the other two models for both within and out of

samples. Furthermore, in terms of consistency, sensitivity and prediction power

of these models in predicting financial crises within three shorter crisis

windows, namely the 6, 12 and 18 month crisis windows, the ANN model is

also better than the other two models. Finally, the findings in this thesis support

the argument that financial crises may be predicted and hence preventative

measures may be implemented to deal with potential crises in the future.

ii

TABLE OF CONTENTS

ABSTRACT ……………………………………………………...……………… i

TABLE OF CONTENTS ……………………………………………………… ii

LIST OF TABLES …..…………………………………...……………………… vii

LIST OF FIGURES ……………………………………………………………... x

LIST OF ABRETIATIONS …………….……………………………………... xii

ACKNOWLEDGEMENTS ……………………………………………………. xiii

CHAPTER 1

INTRODUCTION

1.1. Background ……………………………………………..…………………… 1

1.2. Research Purposes and Contributions ………………………………..… 2

1.3. Outline of the Thesis ……………………………………..………………… 5

CHAPTER 2

LITERATURE ON THE EWS MODELS

2.1. Introduction ……………………………...………..………………………… 7

2.2. Theories and Models of Currency Crises …………………..………….…. 8

3.2.1. The First Generation Model …..…………………………………..… 10

3.2.2. The Second Generation Model …………………………….……….. 10

3.2.3. The Third Generation Model ……………………………………….. 12

2.3. The Currency Crisis Models and Predicting Crisis Models .………….. 15

2.4. Predicting Currency Crises and the Role of EWS Models ….…………... 18

2.5. Conclusion …………………...………………………………………………. 23

iii

CHAPTER 3

THE INDONESIAN CURRENCY CRISIS EPISODES

3.1. Introduction ……………………………..…………………...……………… 25

3.2. Reversal of Fortunes: From Miracles to Crises ...………………………... 26

3.2.1. Prior to Crisis ……………………………………..…………………. 26

3.2.2. Indonesia in Crisis ……………………………….…………………. 27

3.2.3. The Impacts of the 1997/98 Asian Financial Crisis …………….. 29

3.3. Identifying the Causes of the 1997/98 Asian Financial Crisis ………….. 31

3.3.1. External Factors: Financial Contagion …………..………………… 31

3.3.2. Internal Factors: the Weakening of Domestic Economic

Fundamentals ……………………………..………………………....

32

3.4. After the 1997/98 Asian Financial Crisis ………………………………… 35

3.4.1. The Long Road to Economic Recovery ….………..………………. 35

3.4.2. The 2008/09 Global Financial Crisis ……………………………… 39

3.4.3. The Comparison between the 1997/98 Asian Financial Crisis

and the 2008/09 Global Financial Crisis …..………………………

41

3.5 Defining a Currency Crisis ……………………………………………….... 44

3.6. Conclusion …………………………………………………………………… 47

CHAPTER 4

PREDICTING INDONESIAN CURRENCY CRISES USING

THE SIGNAL MODEL

4.1. Introduction …………...…………………………………………………… 49

4.2. A Survey of Empirical Signal EWS Models ……………………………… 49

4.3. Methodology ………………………………………………………………… 52

4.3.1. Selecting Leading Indicators …………………………...………….. 53

4.3.2. Constructing a Composite Index ………..………………….……… 56

4.3.3. Generating the Probability of a Currency Crisis ………………… 57

4.3.5. Model Performance Evaluation …………………………………… 57

iv

4.4. The Application of General Signal EWS Model for Predicting

Indonesian Currency Crises ………………………………………………

60

4.4.1. Constructing the Signal EWS Model …………………………….. 60

4.4.2. Predicting Indonesian Currency Crises ………………………….. 64

4.4.3. The Signal Model’s Performance Evaluation ………..…………… 69

4.5. Assessing Sector Specific Forecasting and the Crisis Channels ……… 71

4.5.1. Capital Account Sector Specific Signal EWS Model ……………. 71

4.5.2. Current Account Sector Specific Signal EWS Model …………… 73

4.5.3. Financial Sector Specific Signal EWS Model …………………….. 75

4.5.4. Fiscal Account Sector Specific Signal EWS Model ………………. 76

4.5.5. Global Economy Sector Specific Signal EWS Model ……………. 77

4.5.6. Real Sector Specific Signal EWS Model ………………………….... 78

4.5.7. Performance Evaluation for Sector Specific Forecasting Results .. 79

4.6. Conclusions ………………………………...………………………………... 81

CHAPTER 5

PREDICTING INDONESIAN CURRENCY CRISES USING

THE DISCRETE CHOICE MODEL

5.1. Introduction …………………………………………………………………. 87

5.2. Literature Review ……………………………………..…………………….. 88

5.3. The Discrete Choice Probit/Logit Model ………………..………………. 92

5.3.1. The Probit Model ………………………………………….………… 93

5.4. The Application of the Probit/Logit EWS Model for Predicting

Indonesian Currency Crises …………………………………..……………

94

5.4.1. Constructing the Probit/Logit EWS Model ………………….…… 94

5.4.2. Estimation Results …………………………………..……………… 99

5.4.3. Predicting Indonesian Currency Crises …………….……………. 102

5.4.4. The Probit EWS Model’s Performance Evaluation ……………… 107

5.5. Conclusions ….………….………………………..………………………… 110

v

CHAPTER 6

PREDICTING INDONESIA CURRENCY CRISES USING

THE ARTIFICIAL NEURAL NETWORK MODEL

6.1. Introduction ………………………...………………………..……………… 112

6.2. Literature Review on the Application of the ANN EWS Model ……… 112

6.3. Specification of the ANN Model …………………………………..……… 117

6.3.1. Architecture of the ANN Model …………………………………… 117

6.3.2. The Learning Algorithm …………………………………………… 120

6.4. The Application of the ANN EWS Model for Predicting Indonesian

Currency Crises ………………………………..………………………….…

125

6.4.1. Constructing the ANN Model ………………………..................... 125

6.4.2. Predicting Indonesian Currency Crises ………………………….. 135

6.4.3. The ANN EWS Model’s Performance Evaluation ……………… 139

6.5. Conclusions ………………….……………………………………………... 142

CHAPTER 7

EARLY WARNING SYSTEM MODELS: COMPARISON

AND CONSISTENCY

7.1. Introduction ……………..……………………………………………...…… 151

7.2. Modelling Results Using a 24-Month Crisis Window ...……………….. 152

7.2.1. In-Sample Prediction Using a 24-month Crisis Window ……….. 152

7.2.2. Out-of-Sample Prediction Using a 24-month Crisis Window...…. 155

7.3. The Sensitivity Tests for Shorter Crisis Windows …………………....... 159

7.3.1. The Signal Model ….…………….………………………………….. 160

7.3.2. The Probit Model ……………………………..……………………... 179

7.3.3. The Artificial Neural Network Model ……..……...……………… 187

7.4. Model Comparison Using a Shorter Crisis Window ……………………. 198

7.4.1. In-Sample Prediction Using a 12-month Crisis Window .……….. 199

7.4.2. Out-of-Sample Prediction Using a 12-month Crisis Window ….. 202

7.5. Conclusions …………….…………..……………………………………….. 207

vi

CHAPTER 8

CONCLUSIONS

8.1. The Main Findings ……………………..…….………..…………...………. 219

8.2. Directions for Future Research ………………………………………….…. 221

BIBLIOGRAPHY ……………………………..………………….……………… 222

vii

LIST OF TABLE

TABLE 3.1 GDP Growth of Asia-5 (% per annum) ………………………... 27

TABLE 3.2 Moody’s and Standard and Poor’s Long Term Debt Ratings for Indonesia Prior to Asian Financial Crisis, 1996-1997 …….

27

TABLE 3.3 GDP Growth by Sectors, 1996-2007 ……………………………. 38

TABLE 3.4 The Difference between the 1997 Asian Financial Crisis and the 2008/09 Global Financial Crisis on Indonesian Economy..

43

TABLE 3.5 The Indonesian Crises Episodes Based on Previous Studies… 47

TABLE 4.1 Performance Matrix of Early Warning Indicator …………….. 55

TABLE 4.2 The Performance Evaluation of Individual Indicators ………. 61

TABLE 4.3 Composite Index and Probabilities of a Currency Crisis ……. 64

TABLE 4.4 The General Signal Model’s Performance Evaluations …….… 70

TABLE 4.5 Performance Evaluation of the Sector Specific Signal EWS Models …………………………………………………………….

80

TABLE 4.6 The Forecasting Results on Indonesian Currency Crises, 1970-2008 ………………………………………………………………..

81

TABLE A4.1 The List of Leading Indicators ………………………………….. 82

TABLE A4.2 The List of Leading Indicators for the Capital Account …….. 85

TABLE A4.3 The List of Leading Indicators for the Current Account ……. 85

TABLE A4.4 The List of Leading Indicators for the Financial Sector ……… 85

TABLE A4.5 The List of Leading Indicators for the Fiscal Account ………. 86

TABLE A4.6 The List of Leading Indicators for the Global Economy …….. 86

TABLE A4.7 The List of Leading Indicators for the Real Sector …………… 86

TABLE 5.1 List of Explanatory Variables …………………………………… 97

TABLE 5.2 The Descriptive Statistics ............................................................... 98

TABLE 5.3 The Correlation Matrix ………………………………………….. 99

TABLE 5.4 The General Model (Model 1)’s Regression Results …………. 100

TABLE 5.5 The Specific Model (Model 2)’s Regression Results …………. 102

TABLE 5.6 Determinants of Indonesian Currency Crises ………………… 102

viii

TABLE 5.7 The Probit Model’s Performance Evaluation …………..….….. 109

TABLE 6.1 List of Input Nodes for Model 1 ……………………………..… 127

TABLE 6.2 List of Input Nodes for Model 2 ……………………………..… 128

TABLE 6.3 Elements of Artificial Neural Network Architecture ……..… 133

TABLE 6.4 Average Contribution of Input Nodes to Output Node for Model 1 ………………………………………………………….…

134

TABLE 6.5 Average Contribution of Input Nodes to Output Node for Model 2 …………………………………………………….………

134

TABLE 6.6 The ANN Model’s Performance Evaluation ………..………… 141

TABLE A6.1 The Weights and Adjustment Weight from Input to Hidden Layers for Model 1 …………………………………..……………

144

TABLE A6.2 The Weights and Adjustment Weight from Hidden to Output Layers for Model 1 ………………………………………

147

TABLE A6.3 The Weights and Adjustment Weight from Input to Hidden Layers for Model 2 …………………………….…………………

148

TABLE A6.4 The Weights and Adjustment Weight from Hidden to Output Layers for Model 2 ………………………………………

150

TABLE 7.1 In-sample Evaluation Using a 24-month Crisis Window ……. 155

TABLE 7.2 Out-of-Sample Evaluation Using a 24-month Crisis Window 158

TABLE 7.3 The Signal Model with Fixed NSR’s In-Sample Evaluation …. 163

TABLE 7.4 The Signal Model with Fixed NSR:’s Out-of-Sample Evaluation …………………………………………...…………….

167

TABLE 7.5 List Indicators Based on NSR for Various Crisis Windows …. 169

TABLE 7.6 The Signal Model with Adjusted NSR’s In-Sample Evaluation …………………………………………………………

174

TABLE 7.7 The Signal Model with Adjusted NSR’s Out-of-Sample Evaluation …………………………………………………………

178

TABLE 7.8 The Probit Model’s Regression Results for Various Crisis Windows …………………………………………………………..

180

TABLE 7.9 Determinants of Indonesian Currency Crises ………………… 181

TABLE 7.10 The Probit Model’s In-Sample Evaluation ……………………. 184

TABLE 7.11 The Probit Model’s Out-of-sample Evaluation ………………. 188

TABLE 7.12 The Training Parameter for ANN Models ……………………. 189

ix

TABLE 7.13 Average Contribution of Input Nodes to Output Node …….. 190

TABLE 7.14 The ANN Model’s In-Sample Evaluation ……………….……. 193

TABLE 7.15 The ANN Model’s Out-of-sample Evaluation ……………….. 197

TABLE 7.16 In-Sample Evaluation Using a 12-month Crisis Window …… 201

TABLE 7.17 Out-of-sample Evaluation Using a 12-month Crisis Window.. 205

TABLE A7.1 In-Sample Evaluation Using a 6-month Crisis Window …….. 212

TABLE A7.2 Out-of-Sample Evaluation Using a 6-month Crisis Window .. 214

TABLE A7.3 In-Sample Evaluation Using a 18-month Crisis Window …… 216

TABLE A7.4 Out-of-Sample Evaluation Using a 18-month Crisis Window 218

x

LIST OF FIGURE

FIGURE 3.1 The Development of Banks in Indonesia, 1988-1999 ………. 33

FIGURE 3.2 Indonesian GDP Growth, 1991-2007 …………………………. 37

FIGURE 3.3 Indonesian Social Indicators, 1999-2008 ……………………... 39

FIGURE 3.4 EMPI, Thresholds, and Currency Crisis Episodes …………. 46

FIGURE 4.1 Composite Index, 1970-2008 ...………………………….……... 63

FIGURE 4.2 The General Signal Model: In-Sample Prediction ………….. 66

FIGURE 4.3 The General Signal Model: Out-of-Sample Prediction …….. 67

FIGURE 4.4 Probability of a Crisis for Capital Account, 1970-2008 …….. 72

FIGURE 4.5 Probability of a Crisis of Current Account, 1970-2008 …….. 74

FIGURE 4.6 Probability of a Crisis for Financial Sector, 1970-2008 …….. 75

FIGURE 4.7 Probability of a Crisis for Fiscal Accounts, 1970-2008 ……... 77

FIGURE 4.8 Probability of a Crisis for Global Economy, 1970-2008 ……. 78

FIGURE 5.1 The Probit Models: In-sample Prediction …………..……….. 104

FIGURE 5.2 The Probit Models: Out-of-sample Prediction ……..……….. 105

FIGURE 6.1 Architecture of the ANN Model ……………………………… 118

FIGURE 6.2 A Single Hidden Neuron ……………………………………… 120

FIGURE 6.3 Numbers of Hidden Neurons vs. RMS Errors ………………. 131

FIGURE 6.4 The ANN Models: In-Sample Prediction …………..………… 136

FIGURE 6.5 The ANN Models: Out-of-Sample Prediction …………..…… 138

FIGURE 7.1 In-Sample Prediction Using a 24-month Crisis Window ….. 153

FIGURE 7.2 Out-of-Sample Prediction Using a 24-month Crisis Window..........................................................................................

156

FIGURE 7.3 The Signal Model with Fixed NSR’s In-Sample Prediction… 162

FIGURE 7.4 The Signal Model with Fixed NSR’s Out-of-Sample Prediction ………………………………………………………..

165

xi

FIGURE 7.5 The Signal Model with Adjusted NSR’s In-Sample Prediction ………………………………………………………..

172

FIGURE 7.6 The Signal Model with Adjusted NSR’s Out-of-Sample Prediction …………………………….……………………….…

175

FIGURE 7.7 The Probit Model’s In-Sample Prediction ………………….... 183

FIGURE 7.8 The Probit Model’s Out-of-Sample Prediction ……………… 186

FIGURE 7.9 The ANN model’s In-Sample Prediction …………………….. 192

FIGURE 7.10 The ANN model’s Out-of-Sample Prediction ……………….. 194

FIGURE 7.11 In-Sample Prediction Using a 12-month Crisis Window ….. 200

FIGURE 7.12 Out-of-sample Prediction Using a 12-month Crisis Window 203

FIGURE 7.13 In-Sample Comparison Using Various Crisis Windows and Cut-off Probabilities, 1970/71-1995 …………………………..

210

FIGURE 7.14 Out-of-sample Comparison Using Various Crisis Windows and Cut-off Probabilities, 1996-2008 …………………..………

210

FIGURE A7.1 In-Sample Prediction Using a 6-month Crisis Window…...… 211

FIGURE A7.2 Out-of-Sample Prediction Using a 6-month Crisis Window... 213

FIGURE A7.3 In-Sample Prediction Using a 18-month Crisis Window……. 215

FIGURE A7.4 Out-of-Sample Prediction Using a 18-month Crisis Window 217

xii

LIST OF ABBREVIATION

ADB : Asian Development Bank

AFC : Asian Financial Crisis

ANN : Artificial Neural Network

ASEAN : Association of South East Asian Nation

BAPPENAS : Badan Perencanaan Pembangunan Nasional (National

Development Planning Agency)

BI : Bank Indonesia

BPNN : Back-Propagation Neural Network

CDS : Credit Default Swap

CI : Composite Index

EMS : European Monetary System

EWS : Early Warning System

EMPI : Exchange Market Pressure Index

ERM : European Regional Monetary

GDP : Gross Domestic Production

GFC : Global Financial Crisis

GRNN : General Regression Neural Network

GSB : Global Score Bias

HPAEs : High-Performing East Asian Economies

IBRA : Indonesian Bank Restructuring Agency

IMC : Intrinsic Mode Components

IMF : International Monetary Fund

INDRA : Indonesian Debt Restructuring Agency

JITF : Jakarta Initiative Task Force

LDA : Linear Discriminant Analysis

LM : Lavenberg Marquardt

Malari : Malapetaka Lima Belas Januari in 1974 (15 January 1974’s

disaster)

MDG : Millennium Development Goals

xiii

MPR : Majelis Permusyawaratan Rakyat (People’s Consultative

Assembly)

NIEs : New Industrialized Economies

NSR : Noise-to-Signal ratio

Pakfeb : Paket Kebijakan February 1991 (Government Deregulation

Package in February 1991)

Pakjun : Paket Kebijakan Juni 1983 (Government Deregulation Package in

June 1983)

Pakto : Paket Kebijakan Oktober 1986 (Government Deregulation

Package in October 1986)

Pr* : Cut-off-Probability

QDA : Quadratic Discriminant Analysis

QPS : Quadratic Probability Score

Rp : Rupiah

SBI : Sertifikat Bank Indonesia (Bank Indonesia Certificate)

USA : United States of America

yoy : Year-on-year

xiv

ACKNOWLEDGEMENTS

Finally, after all those years, I can finish my thesis. I realize that this thesis

would not have been completed without the support and help form numerous

people and parties. First and foremost, my gratitude to my coordinating

supervisor, Professor Yanrui Wu and my co-supervisor, Professor Nicolaas

Groenewold, who have guided, supported and encouraged me throughout my

PhD tenure in the University of Western Australia.

I also acknowledge the support of Dr Anggito Abimanyu, Dr Irfa Ampri, Yayan

and Dalim (Indonesian Fiscal Policy Office, Ministry of Finance), Dr Brad

Armstrong (Australian Treasury) for encouraging me to continue my study and

Australian Development Agency (AusAid) for providing me a scholarship,

namely the Australian Leadership Award (ALA).

I thank my Indonesian friends (Revalin, Hartono, Wahyu, Gugup, and Dekar),

and my fellow PhD students at Economics Discipline in UWA Business School

for all interesting discussions and the fun we have had during my stay in Perth.,

I would also like to thank Ms Deborah Pyatt and all administration staffs in

Economics Discipline for their supports during my research and Mr Mel Davies

for helping editing the final draft.

Finally, I would like to thank my parents, my wife (Laina) and my children

(Farhan and Naya) who have cherished me with every great moment and

always supported me whenever I needed it. Last but not the least; I would like

to thank the one and only, ALLAH SWT, for giving me the strength and health

and answering my pray to finish my thesis.

Perth, January 2012

1

CHAPTER 1

INTRODUCTION

1.1. Background

Since the 1970s, the number of countries hit by crises has tended to increase.

Examples include the Latin American crises of the 1970s/80s, the EMS crisis in

1992/93, the Mexican crisis in 1994, the Asian Financial crisis in 1997/98, Russia in

1998, Brazil in 1999, Turkey in 2000, Argentina in 2002, Uruguay in 2002, USA in

2007, and the global financial crisis in 2008. Indonesia is no exception. The country

has an open economy and has experienced several financial crises, with the Asian

Financial Crisis of 1997/98 being the most severe to hit the country. Even today,

currency crises are a threat for many countries around the world and will

undoubtedly continue to occur into the future.

The costs associated with these crises are huge. According to Hutchison and Noy

(2002), currency crises can lead to average growth reductions of between 5-8%,

while the decline due to banking crises is on average about 8-10%. Other works

that examine emerging economies have claimed the impact of currency crises can

reduce output growth by 2-3%. Laeven and Valencia (2008) indicated the average

fiscal cost associated with resolving a financial crisis was about 16% of GDP. The

cost of a currency crisis occurring simultaneously with a banking crisis or twin

crises (Kaminsky and Reinhart, 1999), is obviously enormous and can have a

devastating effect upon an economy. In addition to financial chaos and

disruptions, the impact also includes adverse social effects such as increasing the

numbers forced into poverty and unemployment.

In Indonesia the 1997/98 Asian Financial Crisis was such a multidimensional crisis

that it had serious social and political ramifications. Economically, the crisis caused

a decline in economic growth by 13.1%; gross fiscal costs were high and output loss

2

related to banking distress reached 56.8% and 67.9% of GDP (Laeven and Valencia,

2008). The social costs of the crisis were also tremendous, for after experiencing a

remarkable poverty drop prior to the crisis, poverty in Indonesia was estimated to

have risen from 17.72% in 1996 to about 24.2% of the whole population in the wake

of the crisis in 1998 (Balisacan et al., 2002). In absolute terms, this saw

unemployment increase by about 14 million, from about 6 million at the beginning

of the crisis to 20 million at the end. The impact engendered both social and

political disorder, culminating in the Jakarta riots that were followed by Suharto’s

resignation as the President of Indonesia on 21 May 1998.

The severity of these events has motivated many studies of the causes of past

currency crises in an attempt to identify common underlying factors. Such research

it is hoped will help prevent the occurrence of future crises. One of these efforts is

to develop econometric models for detecting a country’s vulnerability to crises.

1.2. Research Purposes and Contributions

In the aftermath of the 1997/98 Asian Financial Crisis, there has been an increasing

desire among scholars, policy makers, and international organizations, to develop

econometric models that can predict currency crises. Thus, the emphasis has been

on developing early warning system (EWS) models. Broadly speaking, there are

many forecasting techniques and methods that are available, but the most widely

used are the signal or indicator approach pioneered by Kaminsky et al. (1998), and

the probit/logit model proposed by Eichengreen et al. (1996) and Frankel and Rose

(1996). However, the results of these models in predicting currency crises are

mixed (Goldstein et al., 2000, Berg and Pattillo, 1999a, Kaminsky and Reinhart,

1998, Edison, 2000, Peltonen, 2006). A major problem in these previous studies is

that they have mainly focused on cross-country or regional analyse that assume all

countries are the same. This means they cannot capture specific characteristics for

each country, which limits their powers of prediction. The objective of this study is

3

therefore to develop EWS models that can predict Indonesian currency crises.

Towards this goal, this study employs two widely used EWS models, namely the

signal approach and probit/logit model, plus one alternative EWS model, the

artificial neural network (ANN) model. Because of the poor predictive capacity of

these standard EWS models in regards to Indonesia, an attempt will be made to

strengthen their prediction power so that they can be used as EWS models to

predict Indonesian currency crises.

In reinvigorating the signal approach, this study analyses only Indonesia, which

allows for greater flexibility, as the number of indicators can be increased

significantly. Subsequently, a set of 55 monthly leading indicators from 1970 to

2008 that can be divided into 6 groups will be utilised. These groups represent the

capital account, the current account, the financial sector, the fiscal sector, the global

economy and the real sector. However, as a non-parametric approach, the signal

approach is unable to define the source of a crisis. To deal with this issue, and

taking advantage of the large number of indicators, a sector specific signal

approach will be developed so as to define the sources and subsequent channelling

of currency crises in Indonesia.

Unlike the signal approach, the probit/logit model cannot employ a large set of

explanatory variables due to the problem of multicolinearity (Zhuang and

Dowling, 2002). This study selects the set of explanatory variables by employing

the noise-to-signal approach. This is commonly used in signal approach research to

evaluate and select the leading indicators by comparing the ability of each

indicator sending more good signals while simultaneously eschewing bad signals.

By using the top ten leading indicators based on this method, this study will

attempt to improve the predictive power of this model to trace Indonesian

currency crises.

The ANN model will also be used as an alternative EWS model, following its

previous success in predictions in other fields, and in order to take up the

suggestion by Edison (2000) and Kaminsky et al. (1998) that new techniques or

4

methods should be applied. Kamruzzaman et al. (2006) found that the ANN model

was better than multiple regressions for real life problem solving, including those

problems associated with finance and manufacturing. Furthermore, based on their

survey of 72 papers applying and comparing ANN and logistic models, Dreiseitl

and Ohno-Machado (2002) found that the ANN model generally outperformed the

logistic model because it had several features that were not available in the

standard models. These features include fault tolerance, generalization, and

adaptability (Medsker et al., 1996).

The ANN model can also be trained to improve its predictions; however, the

quality of the data set is also important here (Walczak and Cerpa, 1999).

Additionally, due to their similarities, this model can also be used as an alternative

to logistic regressions and many studies can be quoted (Tu, 1996, Ottenbacher et

al., 2004, Dreiseitl and Ohno-Machado, 2002). Thus, in order to improve the

predictive performance of this model and also to make it more comparable with

the probit/logit model, this study will select a set of input neurons using the top

ranked indicators that based on the noise-to-signal ratio.

The sensitivity and consistency of these three EWS models will also be tested by

shortening the crisis windows to 6, 12 and 18 month periods, and to define the best

EWS model for predicting Indonesian currency crises, the performance of these

three EWS models across prediction horizons will be compared. Finally, this study

extends the sample periods to 2008, so as to see whether the respective models are

able to capture any periods of vulnerability following the Asian Financial Crisis of

1997/98.

By embracing the above EWS models, utilising 55 monthly leading indicators,

adopting sensitivity and consistency tests, and comparing the ability of these

models over periods of 6, 12, 18 and 24 month windows, it is hoped that predictive

tools can be discovered that will enable periods of currency crises to be identified

in the future, and which might therefore prove valuable in allowing evasive

actions to be taken, especially within Indonesia.

5

1.3. Outline of the Thesis

The next chapter (Chapter 2) summarizes theoretical and empirical studies of

currency crises. It will also discuss the link and implications of currency crises

theories with empirical studies that deal with the application of EWS models. The

chapter will also highlight the advantages and disadvantages of these standard

EWS models. In addition, an alternative EWS model, namely the ANN model, is

included so as to address the limitations of the EWS models. Chapter 3 provides an

overview of the Indonesian economy, particularly in the period before, during and

after the 1997/98 Asian Financial Crisis. It will define currency crises in Indonesia

using the definition adopted by Kaminsky et al. (1998).

The first part of Chapter 4 will summarize the empirical studies that apply the

signal approach to predict crises. The signal EWS model using 55 monthly leading

indicators from 1970 to 1995 will then be adopted to test its predictive power using

the out-of-sample currency crises from 1996 to 2008. In order to define the cause of

crises, this chapter will also develop sector specific signal models.

The fifth chapter develops the probit/logit model for predicting currency crises in

Indonesia. This involves a summary of previous empirical studies on currency

crises in which the probit/logit model has been applied. The noise-to-signal ratio

as used by the signal approach that selects the set of explanatory variables will

then be used. The study will then develop and compare two probit models, namely

the general and specific models as Kamin and Babson (1999) and Kamin et al.

(2007) employed in their studies. These models will be estimated and tested using

data from 1971 to 1995 so as to predict the out-of-sample currency crises from 1996

to 2008.

The sixth chapter employs the artificial neural network model as an alternative

EWS model. This chapter also summarizes previous empirical studies that have

applied this method to predict crises. This chapter will also develop and compare

two models, namely the general and specific models. The general model uses the

6

set of input neurons from the top ten ranked leading indicators that are based on

their noise-to-signal ratio, while the specific model uses five highly significant

explanatory variables based on the probit model. Both models are then trained

using the in-sample period from 1971 to 1995 and tested to predict the out-of-

sample crises from 1996 to 2008.

Chapter 7 evaluates and compares the performance of these three EWS models

using a 24-month pre-currency crisis window for both in-sample and out-of-

sample periods. The second part of this chapter will then test the sensitivity and

consistency of these models when the assumption of a crisis window is shortened

to 6, 12, and 18 months. The latter part of Chapter 7 will also evaluate and compare

the performance of these models in predicting these shortened crisis windows.

Finally, Chapter 8 will summarize the main findings related to the application of

these EWS models for predicting Indonesian currency crises. It will highlight

possible future directions for research that will enable improvement and extension

of the use and application of these EWS models.

7

CHAPTER 2

LITERATURE ON THE EWS MODELS

2.1. Introduction

Throughout modern history, currency crises have impacted upon the fortunes of

many countries. According to Bordo et al. (2001), while currency crises occurred

even in the nineteenth century, their frequency and the number of countries hit by

crises has increased appreciably following the collapse of Bretton Woods in the

1970s. Furthermore, in the last three or four decades, the frequency and the

severity of currency crises have increased due to globalization and the rapid

development of information technology (Saxena, 2004). Furthermore, according to

Bordo et al. (2001), the impact of crisis became a dominant feature in the 1990s,

striking many countries in Europe (ERM crises in 1992/93, Russia in 1998/99),

Asia (Asian Financial Crisis in 1997/98) and Latin America (Mexico in 1994, Brazil

in 1998/99).

The 2007 Subprime Crisis in the United States was followed by the 2008 Global

Financial Crisis. Crisis has become a major issue, attracting a great deal of attention

from multilateral organizations, governments, press and the public, due to the

magnitude of the impact and huge recovery costs of these crises. Today, crises still

pose a serious threat to many countries around the world in general and European

countries in particular. Previously countries that were affected by crisis were

dominated by developing countries, but the recent crises shows that developed

countries are also vulnerable to attacks. In other words, there are no countries,

either developed or developing, that can avert the ramifications of crises.

The increasing frequency of crises and number of countries hit by crises as well as

the magnitude of the impact and the huge recovery costs have encouraged many

8

scholars to explain these phenomena and to determine the common underlying

factors. As mentioned in Chapter 1, since the Asian Financial Crisis, various

models have been developed in order to predict the occurrence of crises. Success in

this area should allow policy makers to take counter-cyclical action in order to

avoid the crises or to minimize the impact. Therefore, this chapter will summarize

previous studies, their approaches of explaining these crises, and will ask if there is

a common determinant. Highlighted will be the contribution of these various

studies to the development of empirical models in predicting crises. Finally, in the

last part of this chapter, various crisis forecasting models used in previous studies

will be discussed and lessons will be drawn in an effort to develop useful models

for predicting currency crises, with particular emphasis being placed on their

ability to cater for Indonesia.

The organization of this chapter will proceed as follows. Section 2.2 discusses the

theoretical literature on currency crises. Section 2.3 analyzes their implication for

the constructing of early warning system (EWS) models. Section 2.4 discusses the

forecasting methods in predicting currency crises. Section 2.5 will conclude this

chapter.

2.2. Theories and Models of Currency Crisis

This section presents a brief review of the theoretical literature on the causes of

currency crises. In general, the series of crises that hit many countries around the

world cannot be said to have a common foundation (Saxena, 2004, Kaminsky, 2006,

Esquivel and Felipe, 1998). According to Kamisky (2006), they can be grouped into

six types of crises of which four are related domestic vulnerabilities, such as

deterioration of the current account, fiscal imbalance, financial crisis, and external

debt crisis; while the other two types are categorized as sudden-stop crisis, which

is caused by massive capital flight, and the self-fulfilling crisis, caused by panic

9

among investors and herd behavior. It is also noted that these various types of

crisis also exhibit differences in the common underlying factors, as well as the

severity of impact and the cost and time of recovery. Furthermore, Kamisky (2006)

points out that the crisis with the worst impact is the financial crisis.

It is noted that crises experienced by developing countries and more advanced

economies tend to differ. In emerging economies a crisis is most commonly

associated with multiple domestic vulnerabilities, while in the more mature

economies external vulnerabilities are the most common cause. Thus, shocks

emanating from international capital markets can cause sudden-stop and self-

fulfilling crises (Kaminsky, 2006).

Also noted is that the main features and nature of the various crises that have

occurred in many countries around the world, including emerging markets and

mature economies, are not the same as they change over time (Saxena, 2004,

Kaminsky, 2006, Esquivel and Felipe, 1998). For examples, Saxena (2004) pointed

out that the crisis, which occurred in Latin America during 1970s and 1980s was

different from the European crisis in 1990s, this being the ERM crisis in 1992/93. It

is also shown that there are common patterns across these crisis episodes (Esquivel

and Felipe, 1998).

In this regards, many papers have been written and various theories have been

developed to explain the causes of speculative attacks on domestic currencies, and

to define the common underlying factors associated with these crises. These papers

and theories can be broken up into three groups or generations, namely the first

generation crisis, the second generation crisis and the third generation crisis

models. The next sub-sections will briefly describe the specific characteristics or

features of these three generations of crisis models.

10

2.2.1. The First Generation Model

The first generation crisis model was inspired by the collapse of the exchange rates

in Latin American countries during the 1970s and 1980s, for example the Bolivian

crisis (1982-85), the Brazilian crises (1983, 1986 & 1989–90), the Chilean crisis (1971-

74), Peruvian crises (1976 & 1987) and the Uruguayan crisis (1982) (Saxena, 2004).

The first generation of speculative attack model is based on the paper of Krugman

(1979) which typically describes the balance of payment crisis in Latin America

during this period. It focuses on the role of inconstant policies (Chinn, 2006),

macroeconomic fundamentals and speculation (Breuer, 2004). Within this

framework, a currency crisis is seen as a sudden fall in the level of foreign reserves

caused by an attack on the domestic currency and the inevitable change in the

exchange rate regime. Thus the currency crisis here is actually the outcome of poor

macroeconomic policy and of rational arbitrage by speculators.

The crises occurred when the Latin American countries financed their budget

deficits, mostly exceeding 8–10% of GDP (Saxena, 2004), by increasing central bank

credit to the government (Krugman, 1979, Rangvid, 2001), or by monetizing their

budget deficits (Esquivel and Felipe, 1998). Krugman (1979) argued that this policy

lifted the amount of money supply to exceed the amount of money demand. As a

result, according to Rangvid (2001) the foreign exchange reserves depleted at the

same rate as the increase of domestic credit. As persistent loss in foreign reserves

continued, governments abandoned the fixed exchange rate policy due to a

speculative attack on domestic currency, which finally led to a currency crisis

(Rangvid, 2001, Kaminsky, 2006, Esquivel and Felipe, 1998), or as interpreted by

Krugman (1979), as a balance of payment crisis.

2.2.2. The Second Generation Model

The first generation crisis model fails to explain the European Monetary System

(EMS) collapsis in 1992/93, because the roots of this crisis did not come from the

11

depletion of foreign reserves (Kaminsky, 2006). Moreover Jeanne (2000) mentioned

that the EMS crisis was caused by declining credibility of governments in the EMS

countries when dealing with problems of high unemployment and high interest

rates that resulted from the unification of Germany. Similarly, Kaminsky (2006)

argued that the EMS crisis was caused by the conflict between maintaining the

fixed exchange rate and other objectives of government policies, such as reduction

of inflation and achieving economic growth. While maintaining a fixed exchange

rate regime helped achieve the first goal, it reduced competitiveness, and this led

to recession. In addition, abandoning the peg by devaluing a currency increased

competitiveness and ultimately boosted economic activity and reduced

unemployment. As a result, Jeanne (2000) argued that the EMS crisis encouraged

the emergence of the second generation crisis model but that this was not due to

the problems of economic fundamental alone but rather to the nature of the

relationship between the fundamental and speculative attacks against domestic

currencies.

While the crisis that hit EMS countries was initially triggered by the reunification

between West Germany and East Germany in 1990, in order to improve the living

standard of people of the former East Germany, the government adopted an

expansive fiscal policy. This saw a substantial increase in public spending spurred

by the need for investment in infrastructure and by the rise in unemployment

compensation. This pushed inflation up and placed an upward pressure on real

interest rates in Germany to ease the inflationary pressure. At the same time, under

the European Regional Monetary regime, capital became perfectly mobile across

the European borders, resulting in the high interest rates in Germany attracting

capital inflows from other EMS countries that led to the appreciation of the

Deutsche mark. As a response, the other EMS countries raised their interest rates to

maintain the balance of payments equilibrium. Because the countries were in

recession and suffered high unemployment, the policy of raising interest rates

12

encouraged speculators to attack domestic currencies, such as the British pound,

Italian lira and French franc. This later spread to other currencies as they believed

that it was too costly for the respective governments to maintain the fixed

exchange regime. This crisis is illustrated clearly by Eichengreen et al. (1993) using

a simple two-country model in the tradition of Mundell-Fleming.

Using this crisis model, Obstfeld (1986, 1996) found that the expectation of people

was an important trigger for crises. Similarly Flood and Marion (1999) argued that

the shift in market expectation also caused government trade-offs that created self-

fulfilling crises. For example although countries had sound macroeconomic

fundamentals, as people expected currency devaluation in the near future, they

placed enormous pressure on local currency by converting their currency to a

foreign currency before the central bank devalued its currency. This eventually

depleted the foreign reserve used by central banks to defend their currency – thus

a self-fulfilling crisis. Furthermore, the 1992/93 EMS crisis with the feature of ‘self-

fulfilling’ also indicates that a speculative attack can happen even when countries

have sound macroeconomic fundamentals, as mentioned by Flood and Marion

(1999).

2.2.3. The Third Generation Model

Of the three generations mentioned earlier, the first-generation model was inspired

by Latin American crises in the 1970s and 1980s that focused on monetary and

fiscal crises, while the second-generation crisis model, inspired by the EMS crisis in

earlier 1990s, focused on trade-off of government policies and the features of self-

fulfilling crises. On the other hand, the Mexican crisis in 1994 and the Asian

Financial Crisis in 1997/98 encouraged the emergence of a new crisis model

(Kaminsky, 2006). The third-generation of currency crisis models focused on the

issue of contagion and self-fulfillment (Berg and Pattillo, 2000), these being

triggered by the moral-hazard and imperfect information relating to the economic

boom, international lending and asset price bubble (Saxena, 2004, Kaminsky, 2006).

13

Furthermore, Berg and Pattillo (2000) argued that the herding behavior of investors

could have caused the contagion effect that spread crises in Asian countries in

1997/98 through the financial market. This crisis model also highlights the closed

relationship between currency crises and banking crises (Saxena, 2004, Kaminsky

and Reinhart, 1999).

However, according to Berg and Pattillo (2000) the causes and the underlying

factors that led to these crises both in the Mexico crisis in 1994 and the Asian

Financial Crisis in 1997/98 are different. The Mexican crisis originated from the

self-fulfilling crisis that was driven by deteriorating domestic fundamentals such

as the appreciation of the Peso. This led to a current account deficit coupled with

high external debt. This was dominated by short-term external debt for both

government and private sectors, leaving them vulnerable to speculative attacks on

the Mexican peso (Berg and Pattillo, 2000). On the other hand, the occurrence of

the Asian Financial Crisis and the severity of its impact surprised many parties as

for decades these countries had experienced high and stable economic growth and

sound fiscal conditions.

It is believed that the Asian Financial Crisis was caused by weaknesses in the

corporate and financial sectors (Sharma, 1999, Berg and Pattillo, 2000, Radelet and

Sachs, 1998a) that suffered because of high exposure of their debt to the currency

imbalances. According to Radelet and Sachs (1998a), the problem in these countries

initially started when the Asian countries applied financial reform that liberalized

their financial sectors, particularly banking sectors. Unlike the developed

countries, in emerging economies, including the East Asian countries, the banking

sector played a pivotal role in providing the funds for the private sectors compared

to the capital markets (Berg and Pattillo, 2000). Compounding the problem was

that the number of banks increased significantly, a feature that boosted credit

expansion and capital inflows. The fixed exchange rate regime (Basri and Rahardja,

2010, Radelet and Sachs, 1998a) and low international interest rate also encouraged

14

the banking and corporate sectors to borrow abroad. The high capital inflows into

these countries was also triggered by the sound performance of Asian economies

for a few decades previous to the crisis periods, while at the same time Europe and

Japan experienced relatively weak economic performance (Webber, 2001).

According to Radelet and Sach (1998a) and Webber (2001) financial liberalization

that is followed by excessive credits and huge capital inflows can increase the

vulnerability of a financial sector. As the huge capital inflows to the East Asian

countries was dominated by private and commercial bank lending and equity

investment, largely short-term and mostly un-hedged, to finance unsound projects

or non-productive sectors and long-term projects, such as real estate, this created

price bubbles and increased debt exposure to the currency imbalance (Miller and

Luangaram, 1998, Webber, 2001). This situation increased the vulnerability of the

East Asian economies through their financial sector as it increased the exposure of

these economies to capital outflows whenever the perception of investors and

confidence shifted (Miller and Luangaram, 1998, Webber, 2001).

As mentioned by Miller and Luangaram (1998), overinvestment and overvaluation

led to weaknesses in corporate sectors and combined by inadequate policy

responses taken by governments in addressing problems in the banking sector led

to sudden loss of confidence which triggered systemic panic among investors and

created self-fulfilling crisis. Furthermore, according to Webber (2001) because of

the flight to safety, investors started to withdraw their short-term funds. This led to

sharp depreciation in the domestic currency, followed by the fall value of property

and equity values.

In addition to the domestic factors, some scholars have indicated that external

factors also contributed to the occurrence of this crisis. For example, the rise of

world interest rates in 1994 made the difference between interest rates smaller, a

situation that shifted the perspective of investors inducing them to relocate their

15

funds from developing countries to developed countries – a case of ‘flight to

quality’. In addition, Esquivel and Felipe (1998) argued that before the occurrence

of the Asian Financial Crisis, Asian countries experienced a combination of

currency appreciation and deterioration in their current accounts. According to

Miller and Luangaram (1998), Caramazza et al. (2004) and Webber (2001), as the

East Asian currencies were pegged to the US dollar, when US dollar appreciated

against the Yen by 50% from 1995 to 1997, this caused the East Asian currencies to

appreciate. This, in turn, reduced their competitiveness against China and Mexico

through NAFTA, and combined by a slowdown in world trade and weak growth

in Japan this caused a decline in their exports, which later led to deterioration in

the current account balance. This was particularly the situation that occurred in

Thailand and Korea. As mentioned by Edwards (1989) the combination of the

currency overvaluation and the current account deterioration led to currency

devaluation. As their currency was pegged to the US dollar, investors might have

assumed that maintaining the peg would be costly for the government, thus

driving down the foreign reserve and triggering a speculative attack. The result

would be a self-fulfilling crisis, placing additional pressure on the domestic

currency, and leading to a financial crisis.

2.3. The Currency Crisis Models and Predicting Crisis Models

Following the Asian Financial Crisis, studies not only focused on explaining the

occurrence of crises and defining the underlying causal factors but tended to

develop models to predict the occurrence of crises. But, these efforts cannot be

separated from the previous studies on explaining crises because according to

Sharma (1999) the provision and selection of data sets is crucial and a challenging

task when developing EWS models, and the EWS model performance is also

largely determined by the availability of such data. The previous section has

16

explained that not only do crises differ and change over time but that the factors

that cause crises, can be divided into three generations of crisis models. This

section focuses on the implication of these three crisis theories on development of

EWS empirical models by suggesting the variables that might be leading indicators

in predicting crises.

As previously mentioned, according to this first-generation crisis model, a crisis

occurs when foreign reserves persistently decline because of a monetary budget

deficit that leads to a speculative attack on the domestic currency. Based on the

crisis model, the relevant leading indicator is fiscal imbalance and credit to the

public sector (Kaminsky, et al. 1998). In addition, Esquivel and Felipe (1998) found

that seignorage, RER misalignment, the current account balance, and the log of

M2/reserves, are significant variables for the first-generation crisis model.

In addition, related to the empirical work of predicting financial crises, the second

generation crisis model suggests some leading indicators that may be useful for

preventing and predicting financial crises. As already noted, the second-generation

crisis model was inspired by the ERM crisis in 1992. This crisis was caused by the

conflict of the objectives of the authorities and the actual policies adopted.

Therefore, the variables that potentially lead to this crisis can be used as the

leading indicators in predicting the currency crises, especially the crisis based on

this second-generation model, such as output, domestic and foreign interest rate,

unemployment rate, inflation rate, the amount and composition of external debt,

financial fragility, etc. In addition, as mentioned by Abiad (2002) and Kaminsky et

al. (1998) the real exchange rate, the trade or current account balance, and real

wages have the potential to increase the likelihood of a crisis. Similarly, Esquivel

and Felipe (1998) found that negative terms of trade shocks, negative per capita

income growth, and a contagion effect coupled with the leading indicators of the

first-generation crisis model can explain the occurrence of this kind of crises. This

17

crisis model also emphasizes the feature of self-fulfilling, which means that crisis

can occur without the change in fundamentals.

The third generation crisis model also attempts to explain the occurrence of the

Asian Financial Crisis in 1997/98. Prior to the crisis, the Asian countries

experienced high economic growth and robust fiscal conditions, therefore many

scholars pointed out that in this crisis the fundamentals played an insignificant

role. As mentioned by Kaminsky et al. (1998), prior to this crisis, these countries

only experienced a few of this set of eleven leading indicators, which commonly

present themselves prior to a crisis, such as a low level of foreign reserves, severe

currency crises appreciation, high domestic credit growth, high proportion of

credit to the public sector, high domestic inflation, deterioration of the trade

balance, declining exports, excessive money growth, a low ratio of foreign reserves

to narrow money, declining real GDP growth, and increasing public deficits. As a

result, many scholars point out that this crisis was generally caused by the

contagion effect and a self-fulfilling crisis through financial markets. Berg and

Pattillo (2000) found that the only drawback was in their corporate and financial

sectors. Therefore, the leading indicators that can be used to explain and predict

the occurrence of such crisis would certainly be related to these two sectors.

Chinn (2006) argued that the source of this crisis came from contingent liabilities.

Similarly, Caramazza et al. (2004) argued the large short-term obligations, maturity

mismatch of liabilities, a low ratio of foreign reserves to short-term external debt

can increase the risk of having crises due to investor shifts that lead to self-

fulfilling. Kaminsky (2006) pointed out that high foreign debt levels, or indicators

related to fiscal crises, such as government deficits, or even indicators related to

stock and real estate market booms and bursts can be used as good leading

indicators for the third-generation crisis model.

18

Kaminsky (1998) and Caramazza et al. (2004) also argued that the risk of crises

increased in the countries with weak domestic banking systems. Kaminsky (1998)

lists the leading indicators related to the banking problems, including the relative

price of bank stocks, the proportion of nonperforming loans, central bank credit to

banks, and a large decline in deposits. Moreover, the political variables can also be

used as a leading indicator that may have increased the uncertainty that led to the

investor shift (Kaminsky, et. al, 1998).

Caramazza et al. (2004) argued that the financial linkages and weaknesses, such as

reserve adequacy and maturity of bank liabilities, can be used to explain the

contagion effect that helped spread crises from one country to others. In addition,

Kaminsky (1998) and Caramazza et al. (2004) also argued that the crisis in a

neighboring country can increase the risk exposure in the other neighboring

countries, thus encouraging investor panic, leading to a self-fulfilling crisis related

to a flight to safety that leads these countries into crisis.

2.4. Predicting Currency Crises and the Role of EWS Models

The severe impact of the Asian Financial Crisis and the Mexico crisis in the mid

1990s put a big question mark on the effort to define the crises and to determine

the underlying factors that caused them, and possibility to predict them (Esquivel

and Felipe, 1998). Since these crises, the study about a currency crisis has not only

focused on explaining the occurrence of crises and defining the underlying factors

that caused the crisis itself, but has extended to developing EWS models to predict

a crisis. This study follows that path.

Related to the empirical work for predicting currency crises, the three generations

of crisis models highlighted in the previous section, may suggest a set of

explanatory variables that are important in developing a EWS model to predict a

19

currency crisis. These previous studies indicated that crises are different and by

nature the underlying factors of currency crises change overtime. Until today there

are three generations of crisis models. The latest generation crisis model comes

with the features of the contagion effect and is self-fulfilling. According to this

model, crises can occur without any change in fundamentals, which makes the

effort to predict the crises much harder and even multilateral institutions like the

World Bank and the Asian Development Bank and rating agencies were not able to

predict the Asian Financial Crisis. In fact, a few months prior to the crisis, they still

estimated the East Asian countries would experience stable economic growth for

the rest of the 1990s.

In addition, the rapid development in information technology coupled with rapid

integrated global financial markets implies that a crisis in one country can increase

the likelihood of a crisis hitting other countries faster, consequently, the least time

available for the authority to prevent the occurrence of a crisis. Moreover, as

indicated by the sub-prime mortgage crisis in USA in 2007, the rapid innovation

and development of financial products, such as derivative products, also increases

the vulnerability of one country to fall into crises. The above challenges make the

effort to develop a EWS model to predict crises even more difficult.

According to Berg and Pattillo (2000) the EWS model can combine all information

from various sets of explanatory variables or leading indicators into a single

vulnerability index. Basically the EWS model can provide information about the

probability of a crisis or early information on the vulnerability index of one

country before a crisis occurs. Based on these information, the authorities or policy

makers can have time to take counter-cyclical actions in order to eliminate the

crisis, or at least to reduce its impact. As mentioned by Esquivel and Felipe (1998),

good policies can prevent the occurrence of a crisis, in this sense, the EWS model

can help to prevent the recurrence of currency crises. As previously mentioned,

after the Asian Financial Crisis, a number of models have been created that attempt

20

to help decision makers in predicting future crises, but, broadly speaking, two of

the most popular EWS models are the signal approach, which is a non-parametric

approach, and the probit/logit model, which is the parametric approach.

The signal approach was pioneered by Kaminsky and Reinhart (1996) and

Kaminsky et al. (1998). According to Kaminsky (2006) the basic idea of this model

is based on the fact that the set of leading indicators behave differently during the

crisis and tranquil periods, and the anomalous behavior of the set of leading

indicators prior to the crisis period can be used for predicting a currency crisis.

Using this method, the anomalous behavior of these indicators can be transformed

into warning signals if they pass their specific thresholds, allowing these weighted

signals of selected leading indicators to be transformed into a single composite

index. Furthermore, for ease of interpretation, the composite index is converted

into the probability of a crisis.

The application of this model is simple and straightforward as it does not need to

put restrictions in its data sets (Sharma, 1999). As this model first analyzed each

leading indicator individually before combining them into one composite index,

this model can inform the list of deviant behavior of its leading indicators, as well

as the overall probability of a crisis. Thus policymakers can pay more attention to

this set of leading indicators as they contribute more to the vulnerability. In

predicting a crisis, the signal model is preferred when using a long and high

frequency data set (Zhuang and Dowling, 2002), and also can use a large number

of leading indicators, as mentioned by Kaminsky et al. (1998) and Eliasson and

Kreuter (2001) because it ignores the correlation among leading indicators.

However, according to Abiad (2002) this model also has some drawbacks, such as

it cannot provide the marginal effects of its leading indicators, and cannot

distinguish two or more leading indicators that move together. Neither can it be

tested using a statistic test, which makes it more difficult to compare its

performance with others. In addition, there is no special software dedicated to this

21

model. A more detailed explanation of this signal model and the application of this

model for predicting the Indonesian currency crises will be presented in Chapter 4.

On the other hand, the application of the discrete choice model, that is the

probit/logit model in estimating models of currency crises, was popularized by

Eichengreen et al. (1996) and Frankel and Rose (1996), and was then adopted by

other scholars. This model not only estimates the probability of a crisis, but it can

also inform the statistically significant explanatory variables that determine the

probability of the crisis itself (Sharma, 1999). Another advantage of this model is

that it can evaluate all explanatory variables simultaneously (Sharma, 1999, Abiad,

2002) and can inform the marginal effects of these variables relative to the

probability of a crisis (Komulainen and Lukkarila, 2003). In addition, this model

can be estimated using standard econometric or statistical softwares (Abiad, 2002).

However, unlike the signal approach, this model also has some disadvantages, for

using too many explanatory variables may lead to multicollinearity problems

(Jacobs et al., 2005, Zhuang and Dowling, 2002), and a potential increase of noise in

the estimation results and the number of statistically insignificant explanatory

variables (Kamin et al., 2007). Furthermore, using a high frequency data set, such

as monthly data, also has the potential to make noise in estimation results due to

imbalances in the sample. This is because of too few months being included in

crisis periods compared to the number of months in tranquil periods (Esquivel and

Felipe, 1998). In addition, according to Kaminsky et al. (1998) this model cannot

measure and rank their explanatory variables based on their ability to predict

crises. A more detailed explanation of this model, the survey of previous studies

using this model in estimating currency crises, and the current application of this

model in predicting Indonesian currency crises can be seen in Chapter 5.

Even though the previous studies applied these standard EWS models, it is argued

that these models have succeeded in identifying vulnerabilities. Esquivel and

22

Felipe (1998) indicated that currency crises can be predicted, for their model is able

to predict currency crises in most of their samples. However, Sharma (1999) argued

that predicting the timing of a crisis is very difficult, while Chinn (2006) argued

that crises are not entirely predictable. Similarly, Berg and Pattillo (1999a) pointed

out that their prediction models only just outperformed random guessing.

However, when using the long horizons, Berg and Pattillo (2000) found that while

the performance of these EWS models in predicting currency crises has so far has

been mixed, as on one side these models are able to capture the potential risk of

crises but on the other the models still produce lots of false alarms.

Due to the mixed prediction capacity of the timing of crises, these models cannot

substitute the instinctive judgment that has been widely practiced by policy-

makers (Bussiere and Fratzscher, 2002, Zhuang and Dowling, 2002). For this

reason, as mentioned by Edison (2000) and Kaminsky et al. (1998), many

economists and scholars have attempted to find alternative models. Accordingly,

this study has also developed an alternative EWS model in predicting currency

crises, especially for Indonesia, by applying an artificial neural network (ANN)

model.

The selection of this model is more due to the limited application of this model in

predicting currency crises than its success stories when used for predicting

purposes in other fields. This is because it is a non-linear model that has superior

features, not found in the standard models, such as fault tolerance, generalization,

and adaptability. In establishing a EWS model, the availability and reliability of

data sometimes become a major constraint, particularly when the analysis needs

long term historical data. However, this problem has limited impact on the

application of the ANN model because of its ability to deal with erroneous,

incomplete or missing, fuzzy or noisy input data (Kamruzzaman et al., 2006).

Moreover, unlike other models, the ANN model can be trained to obtain any

desired accuracy results. But this model has some drawbacks too, especially as it

23

requires large computational analysis and has a tendency for over-fitting. In

addition, as a black-box model, it cannot explain the causal relationship between

input and output. More detailed explanation of this ANN model, the previous

application of this model, as well as this current study which applies this

alternative model to predict the Indonesian currency crises are presented in

Chapter 6.

2.5. Conclusion

The series of currency crises that hit many countries around the world over many

years are not the same; they can be classified into six types of crises. Similarly the

underlying factors associated with these crises also changed over time. They can be

classified into three generations of crisis models. These crisis models might suggest

a list of explanatory variables or leading indicators that will probably be useful for

enhancing the performance of EWS models. However, according to the latest

generation of crisis models, features of contagion effect and self-fulfilling will

make the effort of predicting a crisis more difficult, as it can occur without a

change in the fundamentals. In addition, the projection becomes more difficult as

the trend of globalization and rapid integration of capital markets makes the time

for policy makers to react become less. Finding the increase in probability of a

crisis becomes more difficult because of the likelihood of crisis in other countries

being transmitted quickly due to the integration of capital markets. The subprime

mortgage crisis in USA in 2007 illustrates that the development and innovation of

financial products or derivative products can also make the effort to predict crisis

much more difficult.

There are various models used by some scholars to predict currency crises, but two

are very popular models, namely the signal and the discrete choice probit/logit

models. However, crises are becoming more difficult to be predicted, although

24

previous application of EWS models indicates that crises can be predicted even if

their results still vary and some prove unreliable. To overcome this problem,

developing new techniques or methods can accelerate the finding of more reliable

methods, and towards that goal, this study employs an alternative EWS model,

namely the artificial neural network. Compared to EWS models, these models

show positive performance but they also suffer from several weaknesses.

25

CHAPTER 3

THE INDONESIAN CURRENCY CRISIS EPISODES

3.1. Introduction

Indonesia, like other countries in the world, is susceptible to speculative attacks

on its currency. Since 1970, the country has experienced a number of crises. Two

have attracted particular attention from the public, namely the 1997/98 Asian

Financial Crisis and the Global Financial Crisis of 2008. The interest associated

with the 1997/98 crisis has been based on the magnitude of the impact, while

the Global Financial Crisis, although not so significant in terms of its impact on

Indonesia, has attracted more public attention. This is because of the process of

government intervention in addressing the crisis, particularly its bank bailout

policy at the end of 2008.

For three decades before the 1997/98 Asian Financial Crisis Indonesia

experienced high economic growth, perhaps explaining why no agencies or

institutions appear to have been interested in estimating the likelihood of this

crisis. However, thereafter, in an effort to prevent a repeat of such incidents, the

attention of academia and international agencies was directed at finding ways

to predict crises. As mentioned in the previous chapter, this study sets out to

build a model to predict currency crises in Indonesia by using an early warning

system (EWS). An initial step in the process of building the EWS model is to

determine the periods of currency crises. Therefore the objective of this chapter

is to provide a brief overview of the currency crises that have occurred in

Indonesia, with special attention being paid to the Asian Financial Crisis of

1997/98, including its impact and the recovery process. The study also attempts

to determine the period of currency crises that will be used in the empirical

models.

The chapter is organized as follows. Section 3.2 describes the reversal of

fortunes - ‘from miracles to crises’. Section 3.3 focuses on identifying the causes

26

of crises. Section 3.4 highlights the long road to economic recovery. Section 3.5

defines the currency crisis dependent variable. Finally, Section 3.6 presents a

summary with concluding remarks.

3.2. Reversal of Fortunes: From Miracles to Crises

3.2.1. Prior to the Crisis

Following 1966 when the New Order regime came to power, Indonesia

experienced a high growth averaging 7.4% per year for the whole period, and

7.6% per year from 1990 to 1997 (see Table 3.1). The figures reflect the positive

impact on the prosperity and improved welfare in the country, which was

characterized by a decreasing level of poverty, rising income per capita,

increased life expectancy and reduced infant mortality. For example, comparing

1966/67 and 1996/97, per-capita income rose from $75 to $1200, the poverty

level decreased from 60% to 11% (or 22 million people), infant mortality

decreased from 118 to 52 per 1,000 births, and the average life expectancy

increased from 48 to 64 years (Baker, 1998).

The World Bank (1993) acknowledged this achievement when including

Indonesia as a newly industrialized economies (NIEs) along with two other

ASEAN countries, Thailand and Malaysia1. The World Bank also recognized the

three countries together with Japan and the “four tigers” of Asia, namely the

Republic of Korea, Singapore, Hong Kong and Taiwan, as being members of the

East Asia miracle, and of the eight high-performing East Asian economies

(HPAEs) (The World Bank, 1993). On the basis of this achievement, the export-

oriented development strategy of all these countries became the role model for

other countries in developing their economies (Wie, 2003).

1 ASEAN stands for the Association of Southeast Asian Nation, which established on August 8, 1967 in Bangkok, Thailand.

27

TABLE 3.1 GDP Growth of Asia-5 (% per annum)

Country 1990 1991 1992 1993 1994 1995 1996 1997 Average (90-97)

Indonesia 9.0 8.9 7.2 7.3 7.5 8.2 8.0 4.6 7.6

Malaysia 9.6 8.6 7.8 8.3 9.2 9.5 8.6 7.8 8.7

Philippines 3.0 -0.6 0.3 2.1 4.4 4.8 5.7 5.1 3.1

South Korea 9.5 9.1 5.1 5.8 8.6 8.9 7.1 5.5 7.5

Thailand 11.6 8.1 8.2 8.5 8.6 8.8 5.5 -0.4 7.4

Source: International Monetary Fund (1998)

By looking at the impressive performance of Indonesia's economy over the

three decades in Table 3.1, it is not surprising that the existence and depth of the

crisis of 1997/98 was not anticipated (Grenville, 2004). Prior to the crisis, there

was no clear signal of the possibility that an economic downturn would occur

because of Indonesia’s supposedly strong macroeconomic fundamentals.

Furthermore, a few months before the crisis, the World Bank (1997) released a

report pointing out that Indonesian economic growth was expected to remain at

7.8% for the remainder of the 1990s. Similarly, the long-term debt ratings

predicted that stable economic performance would occur over the 18 months

run-up to the crisis.

TABLE 3.2 Moody’s and Standard and Poor’s Long Term Debt Ratings for Indonesia Prior to Asian Financial Crisis, 1996-1997

15/1/96 2/12/96 24/6/97 12/12/97

Rating Outlook Rating Outlook Rating Outlook Rating Outlook

Moody’s Foreign Currency Debt

Baa3 Baa3 Baa3 Baa3

S&P’s

October 1997

Foreign Currency Debt

BBB Stable BBB Stable BBB Stable BBB Negative

Domestic Currency Debt

A+ A+ A- Negative

Note: Rating Systems (from highest to lowest), Moody’s: Aaa, Aa1, Aa2, Aa3, A1, A2, A3, Baa1, Baa2, Baa3, Ba1, Ba2, Ba3; Standard and Poor’s ( S&P’s): AAA, AA+, AA, AA-, A+, A, A-, BBB+, BBB, BBB-, BB+, BB, BB Source: Radelet and Sachs (1998b).

3.2.2. Indonesia in Crisis

The Asian Financial Crisis that occurred in 1997/98 stemmed from pressure on

the Thai baht, which forced the Thailand government to float its currency on 2

July 1997. It resulted in regional negative sentiments from neighboring

28

countries including Indonesia. To respond to pressure on the Indonesian

rupiah, on 11 July 1997, Bank Indonesia first widened the intervention band

from 8 to 12%, followed on 14 August 1997 by a change in the exchange rate

system - a shift from a controlled and managed float to a free-floating exchange

rate regime.

In addition, Bank Indonesia implemented a tight monetary policy and

sterilization by raising interest rates from 10.5 to 20%, so as to prevent capital

outflow. But the combination of a large depreciation of the Rupiah, plus high

interest rates, made the crisis deeper (Nasution, 2000), for the decline of the

Rupiah caused an increase in the debt of the private sector which was

dominated by foreign currencies, particularly the US dollar, which was mostly

unhedged (Green and Campos, 2001). Furthermore the implementation of a

tight monetary policy caused many banks to experience liquidity difficulties,

resulting in August 1997 with more than 50 banks failing to comply with the

minimum reserve requirement of 5%. (Djiwandono, 2000). Meanwhile, the

government implemented strict fiscal policy by cutting unnecessary routine

spending, as advised by the IMF, which according to Nasution (2000)

exacerbated the depth of the crisis.

As the crisis deepened, to enhance public confidence and to overcome the crisis,

the government on 8 October 1997, requested help from the IMF through a

fully-fledged standby arrangement. Although initially the public response was

quite positive, things changed after the first instalment of the IMF economic

stabilisation program on 1 November 1997 which set out to liquidate 16 of 26

insolvent banks, constituting 3% of the total assets of the banking sector. As a

result, the banks closure policy, in the absence of a deposit insurance scheme

led to public panic and loss of confidence in the banking industry. This was

followed by a massive bank rush, particularly on private domestic banks, due

to “flight to quality”, which later on boosted liquidity of the state and foreign

private banks. In other words, Indonesia was hit by a banking crisis following

the liquidation of the 16 banks. Furthermore, the capital flight continued to

29

reach $600-700 million per day, causing the Rupiah to depreciate further to a

new low of Rp17,000 per US$1 on 22 January 1998.

In an effort to restore public confidence and halt depreciation of the Rupiah and

the capital flight, the second IMF letter of intent was signed on 15 January 1998.

Then, three months after the closure of the 16 banks in November 1997, the

government on 27 January 2008, finally implemented a blanket guarantee

scheme for commercial banks, domestic and foreign (the obligation included

depositors and creditors). In addition, the Indonesian government established

the Indonesian Bank Restructuring Agency (IBRA) as an independent body

under the Ministry of Finance to deal with bank restructuring. This was in turn

followed by establishment of the Indonesian Debt Restructuring Agency

(INDRA) in September 1998, and the Jakarta Initiative Task Force (JITF) in

November 1998, to deal with the corporate debt restructuring outside the court

system (Manring, 1999).

However, the condition was still inconclusive and became worse due to

widespread student protests and riots in several big cities, especially in Jakarta,

that became known as the tragedy of May 1998 (Tragedi Mei 1998). The events

that occurred from 12 to 14 May 1998, were followed on 21 May 1998 by the

resignation of President Soeharto, after 32 years as President of the country.

3.2.3. The Impacts of the 1997/98 Asian Financial Crisis

The Asian Financial Crisis in 1997/98 that hit Indonesia was the worst crisis to

hit the country since 1970 (Asia Times, 1999). It affected not only on the

economic sector but also other sectors, and had social and political implications.

It has been viewed as a multi-dimensional crisis (Adiningsih et al., 2008).

The economic costs caused by this crisis were devastating. On the fiscal side,

the government budget was under tremendous strain, reeling under the impact

of weaker economic activity; lower export performance was affected by lower

global oil prices; and subsidies increased as a result of the depreciation of the

Rupiah which reached Rp17,000 per US$1 on 22 January 1998. Inflation also

30

climbed to 82.4% in September 1998. In the banking sector, an increasing

number of banks experienced a negative balance during this period, further

affecting their capital base. In October 1998, the equity of the private national

and the seven state banks dropped to the negative zone, and thus became

insolvent. Finally the combination of these factors resulted in a slowing of

Indonesian economic growth in 1997, although it remained positive at 4.6%,

before contracting by 13.1% in 1998.

The social impact of the crisis was also highly detrimental, as it eliminated the

government’s success in overcoming poverty over the previous three decades.

As a result, the proportion in poverty rose from 11.28% or 17.72% (depending

on which measure is taken)2 in 1996 and to 24.2% in 1998 (Balisacan et al., 2002).

The unemployment rate also increased significantly. Furthermore, the impacts

of the economic crisis spilled over, to pose a threat to national security and

integrity. This was mainly due to the rising racial and religious tensions that

accompanied the acceleration of economic difficulties faced by some elements

of Indonesian society. The impact engendered both social and political disorder,

culminating in the Jakarta riots that resulted in the resignation of Soeharto as

President of Indonesia on 21 May 1998 (Martinez-Diaz, 2006, Smith, 2003, Hill,

2007, Soesastro, 2000).

On the other hand, the 1997/98 crisis also contained positive elements, as it

created a more transparent and accountable system of government and politics,

enhanced the decision-making process and also eliminated barriers that led the

way to corporate and financial system reform, as well as reform in the spheres

of law and justice (Manring, 1999). According to Feridhanusetyawan and

Pangestu (2004), this crisis caused a significant change in terms of economic,

politic and social spheres within Indonesia, especially notable was the shift

from an authoritarian regime to a democratic nation. It also saw a shift from a

centralized to decentralized government, and from state led development,

2 This number is based on the new method of poverty calculation held by Statistics Indonesia.

31

oligopoly and monopolistic market structure, to become more open and

competitive.

3.3. Identifying the Causes of the 1997/98 Asian Financial Crisis

The severity of the impact on the society encouraged experts and academics to

examine the underlying reasons for the crisis in an effort to prevent such an

event occurring again. The opinions of experts and academics when

determining the cause of the 1997/98 Asian Financial Crisis can be split into

two, namely the emphasis on external shocks or the contagion effect, and

domestic fundamental weaknesses.

3.3.1. External Factors: Financial Contagion

The 1997 financial crisis in Asia was, in some circumstances, different from the

crises that hit the Latin America countries in the 1980s, or the ERM crisis in

1992. In that sense it calls for a new theoretical framework to be developed to

explain the Asian Financial Crisis of 1997/98 (Kaminsky, 2006). Previously,

crises were deemed to be the outcome of poor macroeconomic policy and of

rational arbitrage by speculators, as explained by the first generation of crisis

models (Krugman, 1979, Flood and Garber, 1984), or as being caused by the

trade-off between the fixed exchange rate policy and other government

objectives, known as the second generation of crisis model (Kaminsky, 2006).

Prior to the 1997 economic crisis, Indonesia experienced high economic growth,

had a robust fiscal position, and had displayed a moderate inflation rate over

many years. So, in the case of the 1997 Asian crisis that hit Indonesia and other

Asian countries, many scholars and economists believed it was triggered by the

contagion effect of the Thai baht crisis that led to regional negative sentiment,

which in turn encouraged speculative attacks on the rupiah and other regional

currencies. That is to say, economic fundamentals played only a small role in

generating the financial crisis.

32

3.3.2. Internal Factors: the Weakening of Domestic Economic Fundamentals

According to this view, prior to 1997, domestic economic fundamentals in

Indonesia were strong enough to allow high economic growth for decades that

was sufficient to allow it to become one of the newly industrialized countries.

The only weakness was found in the financial and corporate sectors that came

to be the channel primarily responsible for the collapse of the Asian ‘miracle’

economies, including Indonesia. Many have blamed the premature opening up

of the Indonesian economy, particularly its financial sector in the 1980s, as the

primary cause of the crisis (Pincus and Ramli, 1998).

At the beginning of the “new order” regime led by President Soeharto,

particularly during the period 1966 - 1980, the government relied heavily on oil

revenues, but when world oil prices fell in the early 1980s, recognised by Hill

(2000) as the end of the oil-financed growth decade, the government began

promoting non-oil exports. To increase competitiveness of the non-oil products,

the government devalued the Rupiah and liberalized the financial system by

issuing several policy packages, namely the package in June 1983 known as

“Pakjun”, and followed up in October 1988 with “Pakto”. Under the former

1983 regulation, the government negated its control over interest rates, removed

subsidies on deposit rates in state banks and credit limits for all banks, reduced

subsidized liquidity credits, replaced the entire ineffective credit ceiling with

monetary instruments and Bank Indonesia certificates known as “SBI”

(Feridhanusetyawan et al., 2000). Furthermore, the October 1988-policy package

gave a greater role to both the local and foreign private sectors to facilitate the

establishment of banks and branch offices.

These policy packages were followed by an increase in the number of banks,

particularly private banks, changes in market structure and competition

between banks, as well as a boost in credit availability for sustaining high

economic growth (Feridhanusetyawan et al., 2000, Pangestu, 2003). For

example, the number of banks more than doubled from 111 in 1988 to 239 in

1996 (see Figure 3.1). Furthermore, the amount of outstanding loans also rose

sharply from Rp12.83 trillion in 1983 to Rp340 trillion in July 1997

al., 2007). The rapid growth of credit

sector, may have been responsible for overheating the economy

economic bubble prior to the crisis

FIGURE 3

Source: Bank Indonesia (various years)

In response to the excessive credit

another policy package in February 1991 known as “Pakfeb”

at tightening liquidity

restrictions such as

before issuing credits

was facilitated by the op

external borrowing climbed significantly

rising to US$55 billion in mid

less than one year (Pincus and Ramli, 1998

On the other hand,

accompanied by the availability of prudent banking regulation and

supervision from Bank Indonesia

7 7

66

14411

30

27

27

0

50

100

150

200

250

1988 1992

State Bank National Private Bank

33

sharply from Rp12.83 trillion in 1983 to Rp340 trillion in July 1997

The rapid growth of credit, which dominantly went

may have been responsible for overheating the economy

prior to the crisis.

3.1 The Development of Banks in Indonesia, 1988

Bank Indonesia (various years)

esponse to the excessive credit allocation, the government

policy package in February 1991 known as “Pakfeb”

tightening liquidity through the banks that were required to introduce

such as minimum capital, and loan to deposit ratio requirements

before issuing credits. This policy led to an increase in foreign borrowing that

was facilitated by the open capital account. As it turns out,

borrowing climbed significantly, resulting in the accumulated debt

billion in mid-1997, with US$34.2 billion payment

Pincus and Ramli, 1998).

hand, the rapid growth in the banking sector

by the availability of prudent banking regulation and

Bank Indonesia (Pangestu, 2003). Problems that emerged were

7 7 7 7 7

161 166 165 164144

39 40 41 41

44

2727 27 27

27

1992 1993 1994 1995 1996 1997

National Private Bank Foreign/joint venture bank

sharply from Rp12.83 trillion in 1983 to Rp340 trillion in July 1997 (Zulverdi et

went to the real estate

may have been responsible for overheating the economy and creating an

Development of Banks in Indonesia, 1988-1999

allocation, the government introduced

policy package in February 1991 known as “Pakfeb”. This was directed

that were required to introduce

loan to deposit ratio requirements,

. This policy led to an increase in foreign borrowing that

en capital account. As it turns out, the private sector’s

the accumulated debt

payment being due in

banking sector was not

by the availability of prudent banking regulation and strong

Problems that emerged were

7 7

130

92

44

40

27

25

1998 1999

Foreign/joint venture bank Regional bank

34

also due to rampant corruption, collusion and nepotism between rulers and

businessmen. According to Schwarz (1994), such conduct was a disease that

struck the Indonesian nation at that time. Many saw such unhealthy practices as

the main cause of the economic crisis in 1997 (Pincus and Ramli, 1998). As

presented by Stiglitz and Greenwald (2003) weak institutional infrastructures,

such as the law, law enforcement and proper and prudent controls, encouraged

banks to take high risks in their operation, leading to moral hazards.

Furthermore, lack of control and weak supervision resulted in a variety of

offenses in the Indonesian banking sector, such as over-issuing credit limits and

bank guarantees, especially to companies that were engaged in funding the

expansion of their subsidiaries (Zulverdi et al., 2007).

Excessive credits thus eventually rendered the economy vulnerable as the

economy overheated. This viewpoint is supported by the study of Demirgue-

Kunt and Detragiache (1998), who studied 53 countries including developed

and emerging markets from 1980 to 1995. They concluded that there was a

strong connection between financial liberalization and financial fragility,

whereby economic fragility occurs a few years after liberalization. Similarly,

Bordo et al. (2001) also mentioned that the financial liberalization and

inefficiency of financial markets in the distribution of resources increase the

frequency of crises and the magnitude of the impact of crises in the 1990s. In

line with this argument, Kaminsky and Reinhart (1999) identified the close

relation between banking and currency crises following financial liberalization.

According to Caprio and Summers (1993), the lack of effective institutions to

oversee prudential regulation and supervision, and the absence of well-

functioning capital markets and stable legal systems, offset the benefits of

financial liberalization by increasing a country’s vulnerability to banking crises.

35

3.4. After the 1997/98 Asian Financial Crisis

3.4.1. The Long Road to Economic Recovery

As mentioned in the previous section, in responding the 1997/98 Asian

Financial Crisis, the Indonesia authorities applied various policies, starting with

widening the intervention band on the Rupiah, adopting a free-floating

exchange rate regime, tightening monetary policy, adopting strict fiscal policy,

and finally, asking for help from the IMF. Furthermore, when restructuring the

severely damaged banking system, the authorities also applied policies such as

bank closures, implemented blanket guarantees, and established new

institutions, such as IBRA (Indonesian Banking Restructuring Agency), INDRA

(Indonesian Debt Restructuring Agency) and JITF (Jakarta Initiative Task

Force). However, compared to other Asian countries affected by the crisis, the

recovery process in Indonesian was much slower. The country’s relatively slow

recovery reflected the scale and complexity of the Indonesian crisis, which

brings into focus the transitional conditions in redefining the Indonesian state

in terms of its economic, political and social life (Feridhanusetyawan and

Pangestu, 2004).

Nasution (2002) argues that the slow process of recovery could have been due

to internal and external factors that followed the financial crisis of 1997/98. For

internal factors, the “new order” regime, characterized by authoritarian and

centralized government, was followed by a transitional democratic and

decentralized era. This was associated with weak central government coupled

with weak fiscal capacity to stimulate the economy because of declining tax

revenues and falling export earnings caused by low world oil prices. In addition

there was an increase in expenditure for external and domestic debt financing

and for a high level of subsidies. The unrecovered banking sector limited its

intermediary function and, coupled with political and social instability, this led

to declining investor interest in Indonesia. Externally, factors such as the

international recession after the terrorist attacks on 11 September 2001 also

contributed to the slow economic recovery.

36

Martinez-Diaz (2006), while stressing that the financial crisis of 1997/98 was the

worst crisis to hit Indonesia, also highlighted the severe political crisis as

contributing to a slow process of economic recovery. In addition, Wie (2003)

mentions the natural disaster that accompanied the “el nino” and which

resulted in a long dry season for many parts of Indonesia, as being another

reason for the slow recovery process. This problem was coupled with the health

problems of President Suharto that reduced public confidence in the ability of

government to overcome the crisis (Manring, 1999).

Moreover, Cole and Slade (1999) argued that the initial policy of the

government in dealing with troubled banks and the closure of 16 banks in

November 1997, exacerbated the crisis and adversely affected recovery. As the

impact of this policy was usually permanent and required immediate

settlement to creditors and debtors, it also had a psychological impact on public

confidence. On the other hand, neighbouring countries had the same banking

problems, but the policies taken to address these issues were different, as they

only froze the troubled banks, resulting in a temporary impact.

The Indonesian economy reached the bottom of the crisis in 1999, after which

macroeconomic stability was gradually restored, although full economic

recovery even today has a long way to go. Compared to other countries in the

region, such as Thailand, Korea, and Malaysia, Indonesia’s economic recovery

was slower. While its economy grew less than 1%, Korea, Thailand, and

Malaysia grew by 9.5%, 4.4% and 6.6% respectively.

After implementing various economic reforms, the Indonesian economy finally

escaped from the crisis and as a result, it came out of the IMF program at the

end of 2003. Since 2004, as shown in Figure 3.2, GDP grew around 5.5% per

year, even though during this period the recovery process was challenged by

various threats from both domestic and external factors. For example, the

political turmoil that followed the dismissal of the fourth President

Abdurrahman Wahid in July 2001; the devastating natural disaster associated

with the earthquakes and tsunami in Aceh and North Sumatra in December

37

2004; the rise of world commodity prices, particularly oil, that pushed the

government to remove the subsidy on the domestic fuel price and to increase

the price twice, in May and October 2005. This became recognized as a mini

crisis (Titiheruw et al., 2009, Imansyah and Abimanyu, 2008).

FIGURE 3.2 Indonesian GDP Growth, 1991-2007

Sources: The World Bank (2011), Statistics Indonesia (2011), Adiningsih et al. (2008)

Despite escalating world oil prices and the continuing fallout from the

‘subprime mortgage’ crisis in the United States, the year 2007 was a special time

for the Indonesian economy. This was because the year not only marked the

tenth anniversary of the Asian economic crisis, but it was the first time the

Indonesian economy grew at pre-crisis rates, when reaching 6.3%; see Figure

3.2. The improved level of purchasing power and investment primarily drove

this achievement.

In terms of supply, the main contributors to economic growth were the

manufacturing, trade and agricultural sectors. While the growth of

manufacturing during 2007 reached a respectable rate of 4.7%, trade, and

restaurant and hotel businesses, increased by over 8.5%. Similarly, the

8,90

7,20 7,30 7,508,20 7,80

4,60

-13,10

0,60

4,90 4,903,80

4,404,90

5,60 5,606,30 6,00

-15

-10

-5

0

5

10

1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

38

agricultural sector enjoyed a positive trend growth of 3.5% as a result of robust

export demand and high prices for international commodities.

TABLE 3.3 GDP Growth by Sectors, 1996-2007

Source: Statistic Indonesia (2011), Bank Indonesia (various years)

Sustainable but modest economic growth rates over the last decade, which hit a

peak in 2007, contributed to reinvigorate the standard of living, as indicated by

per capita income that reached US$1,946. Unemployment also fell from a high

of just over 11% in 2005 to 8.4% in 2008, while 4.5 million new jobs were

provided; see Figure 3.3. In addition, the poverty rate declined continuously to

reach 15.4% in 2008, equal to a reduction of about 1.9 million people living in

poverty. Furthermore according to the Ministry of National Development

Planning/National Development Planning Agency, known as BAPPENAS

(2010), the implementation of poverty alleviation programs in Indonesia proved

successful, as the percentage of people having per capita income less than US$1

per day reached 8.5% in 2007 to then decline further to 5.9% in 2008. This was

remarkably lower than the actual target of 10.3% set by the Millennium

Development Goals (MDG) for 2015.

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

Agriculture 3.14 1.00 -1.33 2.16 1.88 4.08 3.23 4.34 2.12 2.49 2.72 3.50

Mining & Quarrying 6.30 2.12 -2.76 -1.62 5.51 0.33 1.00 -0.89 -4.94 1.59 3.72 2.00

Manufacturing 11.59 5.25 -11.44 3.92 5.98 3.30 5.29 5.33 6.38 4.63 4.58 4.70

Electricity, Gas & Water Supply 13.63 12.37 3.03 8.27 7.56 7.92 8.94 5.88 4.23 6.49 6.07 10.40

Construction 12.76 7.36 -36.44 -1.91 5.64 4.58 5.48 6.67 6.91 7.34 9.09 8.60

Trade, Hotels, & Restaurants 8.16 5.83 -18.22 -0.06 5.67 4.38 3.90 5.30 5.78 8.59 8.95 8.50

Transportation & Communication

8.68 7.01 -15.13 -0.75 8.59 8.10 8.39 11.56 14.02 12.97 13.64 14.40

Finance, Rental & Business Services

6.04 5.93 -26.63 -7.19 4.59 6.60 6.37 7.02 7.90 7.12 5.27 8.00

Services 7.77 4.59 -13.24 0.61 4.86 3.83 4.38 4.88 4.89 5.60 5.55 6.60

GDP 7.77 4.59 -13.13 0.61 4.86 3.83 4.38 4.88 4.89 5.60 5.55 6.30

39

FIGURE 3.3 Indonesian Social Indicators, 1999-2008

Sources: The World Bank (2011), Statistics Indonesia (2011), Adiningsih et al. (2008)

3.4.2. The 2008/09 Global Financial Crisis

Following the high economic growth over the previous few years, early in 2008,

Bank Indonesia (2008) predicted continuing growth of between 6.2 to 7.4% over

the next few years. However, the resilience of the Indonesian economy has since

been challenged by the Global Financial Crisis, originally triggered by the 2007

sub-prime mortgage crisis in the United State of America. According to

Murniningtyas (2009), this crisis may affect the Indonesia economy through the

stock market, and through shortages in the capital market and production. Basri

and Rahardja (2010) point out two channels by which this crisis may affect

developing countries, including Indonesia, namely through financial and trade

channels.

Regarding financial contagion, Titiheruw et al. (2009) state that the Indonesian

financial sector had no direct exposure to the sub-prime mortgage securities, as

no Indonesian banks operated abroad, and furthermore, Indonesian banks are

not allowed to conduct securities transactions in capital markets, and no

47,97

38,7 37,9 38,4 37,336,1 35,1

39,337,17

34,96

23,43

19,14 18,41 18,217,42

16,6615,97

17,7516,58

15,42

6,36 6,07

8,19,06 9,5 9,84

11,2210,28

9,18,4

0

10

20

30

40

50

60

0

5

10

15

20

25

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

# of poverty(in million, RHS) Poverty rate (LHS) Unemployment rate (LHS)

40

investment banks are permitted to operate in the Indonesian banking system.

Even so, this crisis has led to an increased investment risk in emerging markets

and the global liquidity shortage has spread negative sentiments in financial

markets (Bank Indonesia, 2009). Likewise, Basri and Rahardja (2010) have

indicated the impact of this crisis on the Indonesian financial sector is highly

significant. For example, the composite stock price index dropped dramatically

from 2830 on January 2008 to 1155 on 20 November 2008 because of the

withdrawal of a foreign investor who was the dominant player in the

Indonesian stock market when holding 67% of total equity market

capitalisation. As a result, the Rupiah depreciated significantly against the US

dollar, dropping in value by 30% between October and November 2008 (Basri

and Rahardja, 2010). This pressure continued and led to the massive sell-off of

Bank Indonesia certificates (SBI) and government securities This pushed the

increase of the yield of government bonds from 10 to 20% (Ministry of Finance

of Republic of Indonesia, 2010). As a result, and according to Titiheruw et al.

(2009), in the last quarter of 2008, the foreign ownership of government

securities fell sharply from US$11.1 to $8 billion; similarly, the foreign

ownership of Bank Indonesia certificates dropped from US$2.2 billion to $772

million.

In the other channel of the crisis, trade, the pressure on the economy of

Indonesia hit the external sector in which export growth fell to 1.82%, or the

lowest since 1986 (Titiheruw et al., 2009). This was in line with the slowdown in

economic growth of Indonesia’s major trading partners, as well as with world

commodity prices (Titiheruw et al., 2009, McCulloch and Grover, 2010, Basri

and Rahardja, 2010). However, due to the structure of Indonesia’s economy

being dominated by the domestic economy, a decline in exports has not

seriously affected it. The reason for this is that domestic consumption exceeds

external consumption, for as was mentioned by Statistic Indonesia (2011), the

contribution of domestic consumption in the economy reached 69% in 2008 but

only slightly dropped to 68% in 2009. In the same period, exports only

contributed around 30% in 2008 before dropping to 24% in 2009, therefore the

41

decline in exports did not seriously affect the Indonesian economy (McCulloch

and Grover, 2010, Basri and Rahardja, 2010). Moreover, when compared with

neighbouring countries, the impact of this crisis on the Indonesian economy has

been much lower, as its contribution of exports in the economy has been much

lower than in those other countries (McCulloch and Grover, 2010).

3.4.3. The Comparison between the 1997/98 Asian Financial Crisis and the

2008/09 Global Financial Crisis

Although in terms of scale the 2008/09 Global Financial Crisis has been much

bigger than the 1997/98 crisis, especially as it has affected more countries

around the world, its overall impact on the Indonesian economy has been more

limited (Basri and Rahardja, 2010). This is emphasised by McCulloch and

Grover (2010) who claim that the impact of the 2008 crisis on the Indonesian

economy is not large, its impact only causing a slowdown in economic growth

due to a decline in exports. Unlike the 1997/98 crisis which resulted in an

economic slowdown to 4.6% in 1997 followed by a 13.1% economic contraction

in 1998, the recent crisis saw growth at the relatively high rate of 6% until the

third quarter of 2008 after which it dipped in the fourth quarter to 5.2%, a figure

less than the previous estimation of 5.7% (Titiheruw et al., 2009). For the whole

year of 2008 the average rate of economic growth was 6%, after which it

dropped to 4.7% in 2009, thus slightly higher than the low in the previous crisis.

Likewise, the social impact of this crisis was much lower than during the 1997

crisis, for the number of poor fell from 15.42% in 2008 to 14.42% in 2009, or to

2.43 million people. The unemployment level also declined from 8.4% to 8.1% in

2009, although as seen in table 3.4, this was one feature that was higher than the

level reached in 1997/98.

Moreover, Basri and Rahardja (2010) have argued that the limited impact of the

crisis of 2008/09 compared to the 1997/98 crisis has been associated with the

difference in the sources of the crises, the pre–conditions in terms of the

financial sector, the political situation, and policy measures taken by the

government. The lesser impact can also be attributed to management plans

42

implemented by the government that helped to sustain public trust, along with

its efforts to limit depreciation of the Rupiah, and accelerate the recovery

process (McCulloch and Grover, 2010). More detail on the differences between

these two crises can be seen in Table 3.4.

In terms of policy responses taken by government, unlike the 1997/98 crisis, to

overcome the 2008/09 crisis, the government tended to pursue policies that

differed from those adopted in the first crisis. For example, its monetary policy,

whereby Bank Indonesia cut interest rates gradually from 9.5 to 6.5% between

October 2008 and August 2009. Also, in order to boost the economy it

maintained domestic purchasing power and reduced the operating costs of the

business sector. The government also enlarged the budget deficit by 2.6% of

GDP to provide a package of fiscal stimulus valued at Rp73.3 trillion, or US$6.4

billion, through tax reductions, by providing diesel and electricity subsidies,

and by developing infrastructure and rural sector projects (Basri and Rahardja,

2010). In addition, the government also continued the provision of direct cash

transfer programmes for the poor amounting to Rp100000 per month, or US$8,

for 18.2 million of targeted poor households for two months, while at the same

time it increased the salaries of government and military officials and

pensioners (Titiheruw et al., 2009). Those policy decisions are reflected in the

high level of government consumption that recorded growth of 10.4% in 2008

and a further increase to 15.7% in 2009. This led to an increase in the

contribution of government expenditure in GDP formation to nearly 10% in

2009.

When dealing with the banking distress, unlike the previous crisis when the

government closed the troubled banks, the government adopted bailout

strategies to combat the situation. Thus its reaction to the Century Bank where

it intervened in order to avoid a banking crisis, while at the same time avoiding

the psychological impact of bank closures in order to maintain public

confidence in the domestic banking sector. However, a year later, this policy

became a hot political issue between legislators and government, who argued

43

regarding the objectives and the underlying reasons behind that policy

decision. The issue became a trendy topic for the press and public during 2010,

and continues to be discussed today.

TABLE 3.4 The Difference between the 1997 Asian Financial Crisis and the 2008/09 Global Financial Crisis on Indonesian Economy

The 1997 Asian Financial Crisis The 2008/09 Global Financial Crisis

Macroeconomic and Financial Indicators, origin of crisis and political condition

GDP 4.7% 6.1%

Inflation 11.05% 11.06%

External Sector

-Current Account* -2.3% 0.1%

-International reserve 21.4** (5.5***) 51.6** (4.0***)

-Foreign debt* 62.2% 29.0%

Fiscal Account

-Fiscal balance* 2.2% 0.1%

-Public debt* 62.2% 32%

Banking Sector

-LDR (%) 111.1% 77.2%

-CAR (%) 9.19% 16.2%

-NPL (%) 8.15% 3.8%

Origin of crisis Unclear and debatable between external factor (contagion effect from Thai-Bath crisis) and poor economic and financial fundamentals

External factor which is trigger by the sub-prime mortgage crisis from United States of America

Politic condition Unstable due to political crisis Stable

Policy Responses

Monetary policy Strict monetary policy and sterilization by raising interest rate

Reduction interest rate by 300 basis points from 9.5 to 5%

Fiscal policy Tight fiscal policy by cutting unnecessary routine spending to keep budget surplus and later on change to allow budget deficit

Allow budget deficit for providing fiscal stimulus packages by reduction of taxes, and provide subsidies for electricity and fuel, direct cash transfer for the poor and increase the salaries of government and military officials and pensioners

Banking policy Liquidation or closure 16 Banks Then after three months followed by blanket guarantee

Bailout bank i.e. Bank of Century Increase in the coverage of deposit guarantee by Indonesian Deposit Insurance Corporation from Rp100 million to Rp2 billion

Impact of crisis

-Unemployment rate (%)

4.7% (5.5% in 1998) 8.46% (7.9% in 2009)

-Poverty rate (%) 24.2% (in 1998) 15.42 (14.2% in 2009)

-# of poor 49.50 million (in 1998) 34.96 million

-Income per capita (US$)

1,299 2,000

* (%of GDP); ** International reserve (billions of USD); *** (month of imports and official foreign debt repayment; Source: Bank Indonesia (2009), Statistic Indonesia (2011), Basri and Rahadja (2010)

44

3.5. Defining the Currency Crisis

Although the previous sections have discussed the currency crises that occurred

in Indonesia, the exact duration of these crises have not been clearly disclosed.

Therefore, in this section, an attempt will be made to determine the actual

period of financial crisis, as this will be useful as a first and crucial step for

developing the empirical models for predicting currency crises in the following

chapters.

What constitutes a crisis is a crucial factor but there is no agreed definition of a

currency crisis among academics. Some scholars have described a crisis as a

“speculative attack” or “extreme pressure” on the exchange rate that leads to a

substantial sharp change in the exchange rate (Kamin et al., 2007, Frankel and

Rose, 1996). Nevertheless, such definitions only take into account a successful

speculative attack on the exchange rate which shows up in the form of

depreciation of foreign exchange in a free floating exchange rate regime, or that

of official devaluation of the exchange rate in a fixed exchange rate regime.

In the event of a speculative attack on a domestic currency, the central bank can

retain the exchange rate through monetary policies, either increasing official

interest rates or selling off foreign reserves. As a result, unsuccessful

speculative attacks can be seen either in the fall of foreign reserves or in the rise

of interest rates (Krznar, 2004). Following these arguments, Eichengreen et al.

(1995) define a crisis as being large movements in exchange rates, foreign

reserves, and interest rates. However, as many emerging markets had interest

rate controls, Kaminsky et al. (1998) defined a crisis as a situation whereby an

attack on the currency leads to a sharp depreciation of the currency, or to a

large decline in international reserves, or a combination of these two

circumstances. These currency crisis definitions encompass both successful and

unsuccessful attacks on the domestic currency, whether they are under a fixed

exchange rate regime or under other sorts of exchange rate regimes.

45

In light of this, crises are identified by the behaviour of an “exchange market

pressure index”, or, in short, EMPI. Based on Goldstein et al. (2000), the EMPI is

calculated as the weighted average of the change in the direct quoted nominal

exchange rates (ER), and the change in foreign exchange reserves (FR), of which

the weight (ω) is the ratio of the standard deviation of the rate of change of the

exchange rate to the standard deviation of the rate of change of reserves, or as

follows:

�� = �� − �� − �� − �� (3.1)�

Based on this equation, an increase in the EMPI index indicates more pressure

on a domestic currency, which is shown by a depreciated domestic currency or

decline in the foreign reserve or the combination of these conditions.

The next step is to transform EMPI into binary crisis variables in which the “1”

represent a crisis when EMPI is equal or bigger than a given threshold,

otherwise “0” for no crisis. While, the threshold (L) in question is chosen as a

given number of standard deviations from the EMPI’s mean. According to

previous empirical work, the value of the threshold for EMPI varies from 1.5

(Eichengreen et al., 1996) to 3.0 (Goldstein et al., 2000) standard deviations

above the mean of EMPI. Symbolically,

�� = �10� �� ≥ �� < �� (3.2)�

In determining a currency crisis, following Kaminsky et al. (1998) and Goldstein

et al. (2000), the EMPI is calculated using Equation 3.1. Its thresholds are also

calculated using EMPI’s mean plus three EMPI’s standard deviations, as

presented in Figure 3.4. In this figure, the EMPI is presented as a red line and its

threshold is in black. Furthermore, following the work of Kaminsky et al.

(1998), a currency crisis is defined whenever the EMPI passes its thresholds, so

that the yellow shaded area is the 24 months prior to the currency crisis date.

46

FIGURE 3.4 EMPI, Thresholds, and Currency Crises Episodes

Using Figure 3.4, Indonesia experienced four episodes of currency crisis from

January 1970 to September 2008. However, unlike the other currency crises,

during the 1997/98 Asian Financial Crisis, the EMPI crossed its threshold five

times, in August and December 1997, plus January, May and June 1998. The

period of the in-sample currency crises, which was determined by this method

also coincides with the policy of the government of the Republic of Indonesia

which devalued the Rupiah in 1978, 1983 and 1986 in order to support the

export-oriented growth (Goeltom, 2008) and to encourage the performance of

non-oil exports as a response to declining world oil prices in the 1980s (Hill,

2000). As Mishkin (1999) mentioned that in developing countries, currency

devaluation and currency crisis could generate the financial crises through the

corporate debt, which was denominated in foreign currency and at short

maturity, which lead to a deterioration in the balance sheet of the corporate and

banking sectors as well as an increase the inflation rate. In addition, the period

of currency crises determined by using this approach is quite similar to the

period of currency crises in the previous studies in this field, in particular for

the in-sample currency crises, as displayed in Table 3.5.

-0,4

-0,2

0

0,2

0,4

0,6

0,8

1

1,21

/1/1

97

01

/1/1

97

11

/1/1

97

21

/1/1

97

31

/1/1

97

41

/1/1

97

51

/1/1

97

61

/1/1

97

71

/1/1

97

81

/1/1

97

91

/1/1

98

01

/1/1

98

11

/1/1

98

21

/1/1

98

31

/1/1

98

41

/1/1

98

51

/1/1

98

61

/1/1

98

71

/1/1

98

81

/1/1

98

91

/1/1

99

01

/1/1

99

11

/1/1

99

21

/1/1

99

31

/1/1

99

41

/1/1

99

51

/1/1

99

61

/1/1

99

71

/1/1

99

81

/1/1

99

91

/1/2

00

01

/1/2

00

11

/1/2

00

21

/1/2

00

31

/1/2

00

41

/1/2

00

51

/1/2

00

61

/1/2

00

71

/1/2

00

8

a 24-months prior crisis date EMPI Treshold

47

TABLE 3.5 The Indonesian Crises Episodes Based on Previous Studies

No EWS Study Crisis Periods

1. Goldstein et. al. (2000) Nov 1978, Apr 1983, Sep 1986, Aug 1997

2. Zhuang & Dowling (2002) Nov 1978, Apr 1983, Sep 1986, Dec 1997

3. Edison (2000) Nov 1978, Apr 1983, Sep 1986

4. Current Study Nov 1978, Apr 1983, Sep 1986, Aug 1997, Dec 1997, Jan 1998, May 1998, Jun 1998

3.6. Conclusion

Prior to 1997, Indonesia was a country that experienced high economic growth

to become one of the Asian economic miracle countries. But the Asian Financial

Crisis that hit Indonesia became a major catastrophe not only in economic but

also social and political terms. The causes of this crisis have since been debated

in academic circles. Some argue that the crisis was associated with the

contagion effect from Thailand’s crisis while others believe the crisis came from

deterioration of domestic fundamentals, especially in the financial sector

because of financial liberalization in 1980s.

Although the recovery process in Indonesia was initially slow when compared

to other Asian countries affected by crisis, after completing the IMF program in

2003, the Indonesian economy grew around 5.5%, even though the recovery

process was challenged by many obstacles during this period, including the

political crisis of 2001, and an earthquake and tsunami in 2004, plus a mini-

crisis in 2005. After a decade passed, Indonesia was finally able to grow at a rate

similar to that experienced in the period before the crisis, reaching 6.3% in 2007.

But unfortunately the resilience of the Indonesian economy was challenged by

the Global Financial Crisis in 2008, which originally came from the sub-prime

mortgage crisis in United States of America. The crisis affected the Indonesian

economy in the last quarter of 2008 through the financial contagion effect that

downgraded the perception of foreign investors on the investment risk in the

Indonesian financial sector. This was reflected by a massive selling-off of stocks,

48

Bank Indonesia certificates and government securities. This crisis also led to a

decline in exports caused by slowing economic growth in major trading

partners and falling world commodity prices. However, unlike the 1997/98

Asian Financial Crisis, the effect of the recent crisis was not too significant as it

only caused a slowdown in economic growth to 6% in 2008, with a further drop

to 4.7% in 2009.

In determining the period of currency crisis that will be used as the dependent

variable in developing models to predict currency crises in the following

chapters, and based on the method of Kaminsky et al. (1998), this study has

found that Indonesia experienced four currency crises during 1970 to 2008.

49

CHAPTER 4

PREDICTING INDONESIA CURRENCY CRISES

USING THE SIGNAL MODEL

4.1. Introduction

As already explained in previous chapters, the purpose of this study is to

develop models that can predict currency crises in Indonesia. Currently, many

forecasting techniques and methods are available to predict Indonesian

currency crisis episodes using early warning system (EWS) models. However,

this study only considers three EWS models and this chapter is the first chapter

of three that are dedicated to developing these EWS models. For this purpose,

this chapter employs and extends the signal model pioneered by Kaminsky et

al. (1998).

The discussion in this chapter is organized as follows. Section 4.2 describes the

previous literature of the application of the signal EWS model. Section 4.3

explains the signal EWS model including the design of the system, depicts the

scope of the model, and explains the performance evaluation methods to be

used. Section 4.4 focuses on the application of the signal model as a EWS model

for predicting Indonesian currency crises both for the in-sample and out-of-

sample periods. Section 4.5 develops the sectoral specific signal model followed

by concluding remarks in Section 4.6.

4.2. A Survey of Empirical Signal EWS Models

In relation to the empirical work of predicting financial crises using the signal

EWS model, the literature of currency crisis models and theories provides some

leading indicators that may be useful for improving the performance of EWS

models. Kaminsky et al., (1998) conducted an extensive survey of empirical

50

work on currency and financial crises that was aimed at identifying the

potential leading indicators for their work on predicting a currency crisis. The

authors also provided a ranking of leading indicators based on their forecast

capability as measured by their noise-to-signal ratio, plus the time and

persistence of their signals. In other works, Kaminsky and Reinhart (1998) and

Kaminsky (1999) constructed a signal EWS model to predict currency and

banking crises in 20 countries, including 5 developed countries and 15

emerging economies. Their model was able to accurately forecast the 1997/98

Asian financial crisis with the exception of Indonesia.

In related work, Goldstein et al. (2000) extended the research by replacing the

developed countries with other emerging economies and added more leading

indicators. They found that in the case of emerging economies, it was more

difficult to forecast banking crises than currency crises due to the difficulties in

identifying an accurate and recurrent banking crisis. They also showed that

there is a wide discrepancy of performance across leading indicators. To

overcome these problems, they developed a method of aggregating the best

indicators into a composite indicator of a currency crisis. Like the previous

work, they were able to predict the Asian financial crisis for all Southeast Asian

countries with the exception of Indonesia.

In the Indonesian crisis, despite the unstable political condition at that time,

they pointed out that the unavailability of indicators to capture the contagion

effect was the main reason why their model did not send any alarm prior to the

1997 crisis. This argument is also supported by Goldstein et al. (2000) who

indicated that Indonesia had a high contagion vulnerability index related to the

1997 Thai crisis that occurred during the Asian financial crisis.

Edison (2000) extended the signal model developed by Kaminsky et al. (1998)

and Kaminsky and Reinhart (1999) to detect financial crises. She found that the

prediction results were mixed. As Kaminsky (1999) and Goldstein et al. (2000),

she also failed to identify the 1997 crises in Indonesia. Furthermore, Furman

51

and Stiglitz (1998) also employed the signal method to predict the Asian

financial crisis and concluded that it did not work well.

Berg and Pattillo (1999a) evaluated three existing models by comparing their

predictive power to other models including the probit model of Frankel and

Rose (1995), the standard regression model of Sachs et al. (1996), and the signal

model of Kaminsky et al. (1998). Their results found that only one model, i.e.,

the signal model of Kaminsky et. al (1998), was able to predict a crisis; however,

their results are mixed and unreliable. Moreover, their models also fail to

predict the 1997 Indonesian crisis. Nevertheless, they still believe that even

though the predictive powers of these EWS models are limited, they may help

to indicate future vulnerability. Similarly, Edison (2000) highlighted that the

signal EWS model was a useful tool for identifying vulnerabilities.

The above-mentioned studies applied the signal model to predict the crisis

using multi-country data. However, using cross-country analysis can be

advantageous, as researchers can compare the result across countries. Yet, the

selection of indicators can sometimes be limited when trying to capture the

specific characteristic for the whole sample, as compared to a regional or a

single country analysis. In relation to the previous empirical work, this

limitation means that prediction results will be mixed. For Indonesia, most of

their selected leading indicators failed to send any warning signals (Goldstein

et al., 2000).

On the other hand, using regional studies, Zhuang and Dowling (2002)

employed a signal EWS model to identify the source of the Asian financial crisis

during 1997 in six Asia countries: Indonesia, Thailand, Malaysia, Philippines,

Singapore and South Korea. For this purpose, they used a set of 38 leading

indicators and managed to predict the Asian financial crisis in five out of six

countries: Indonesia, Thailand, Malaysia, Philippines, and South Korea. It can

be envisaged that the main source of the crisis in these countries was the

deterioration of domestic fundamentals. However, in the case of Singapore,

their model could not encounter any signal of financial crisis, since the main

52

factor attributed to each crisis suggests financial contagion and investor panic

rather than domestic fundamentals. For Indonesia, they found six crisis

episodes from 1970 to 1997, namely 1970, 1971, 1978, 1983, 1986, and 1997. For

the 1997 financial crisis, its probability remained low from 1987 to 1996 but

started to climb to around 70% for the seven consecutive months before the

Indonesian rupiah depreciated by 21.5% in December 1997. Similar results were

found by Imansyah and Abimanyu (2008) when applying a signal model for

predicting currency crises in Indonesia. Using 22 leading indicators, their model

was able to capture the 1997 currency crisis. These two studies identified that

flexibility in selection of leading indicators can improve the performance of the

model in predicting a crisis. Following this argument and to improve the

predictive power of the signal EWS model, this study will expand the number

of leading indicators and the sample time period to see whether this model can

capture recent crises. Following this, a detailed explanation of the signal EWS

model will be presented in the following section.

4.3. Methodology

There are some important steps in developing the signal EWS model. It starts

with defining the dependent variable of currency crisis, and is then followed by

selecting a set of leading indicators that can determine the currency crisis. The

next step will be to transform the movement of indicators into the warning

signal for each indicator. They will then be combined into one index that will

represent the whole selected leading indicators based on their contributions to

predicting a crisis. Further, the composite index will be converted into the

probability of a crisis to improve the interpretation. Attempting to evaluate the

performance of the model in predicting both in-sample and out-of-sample

currency crises will be the final step.

This chapter is focused on the development of the signal model in predicting

the crisis and the first step is to determine the period of currency crises in

53

Indonesia. However this step is already done in previous chapter, and then this

section focuses on selecting the set of leading indicators, which will be used by

this model in predicting crises.

4.3.1. Selecting Leading Indicators

After determining the crisis variable, the next issue that needs to be addressed

is the identification of the potential leading indicators. The choice of the

indicators is based on theoretical considerations and the availability of high

frequency data, such as monthly data. Knowledge of the sources of currency

crisis provides the basis for identifying possible indicators that will be useful for

developing a model to predict a crisis. According to Kaminsky et al. (1998), an

effective EWS model should include a broad variety of indicators because

currency crises are commonly preceded by multiple economic and sometimes

political problems. Unlike the probit/logit method which is a multivariate

model and can only accommodate a limited number of explanatory variables in

order to avoid multicolinearity (Zhuang and Dowling, 2002), the signal model

is univariate and ignores the correlation amongst independent variables

(Eliasson and Kreuter, 2001).

There are three steps in selecting a set of leading indicators to predict currency

crises. The first step is to generate the signal for each indicator; the second step

is to classify these signals based on their performance to predict a crisis within

prediction horizon; and the final step is to evaluate and select these indicators

using specific selection methods.

The signal model is based on a large number of main economic and financial

variables, which tend to exhibit abnormal behavior before the onset of a crisis.

The abnormal behaviour of one or more leading indicators represents a

warning signal about the possible currency crisis within a specific period of

time. In generating the signals of a potential crisis, the behavior of all indicators,

xit, are transformed into a binary definition of a crisis signal as a “signal” (sit=1),

or “no signal” (sit=0). In empirical work, these indicators demonstrate signals of

54

a potential crisis whenever they depart from their thresholds, because the

thresholds or critical values compartmentalize the distribution of indicators into

a region that is considered as normal from the region that is regarded as

aberrant (Goldstein et al. 2000). If the observed outcome for a particular

variable falls into the abnormal region, the variable in question will send a

warning signal of the presence of a crisis.

Basically the threshold level is chosen on an arbitrary basis in order to

maximize the performance of the indicator when predicting a crisis.

Nevertheless, in determining the value of the threshold, one thing has to be

considered; that is, there is a tradeoff between the risk of having too many false

signals and the risk of missing some crisis exposures. For example, choosing too

low a threshold would result in prompting a number of false signals (noise)

while reducing the number of missed signals. On the contrary, if too high a

threshold is selected, it can reduce the number of false alarms, although at the

risk of missing many signals. Ideally, the choice is to find a balance between

these two sorts of errors.

After generating the signals for each indicator, the next step is to classify these

signals based on their capability to predict a crisis for a specific time horizon or

signaling window (w). Kaminsky et al. (1998) define the time horizon, or

signaling window, or crisis window, as the period during which the indicator

being assessed is expected to display an ability to predict crises. The choice of

the prediction range is arbitrary, and depends on the objectives of the user. For

example, policy makers and public sector movers adopt a relatively long

horizon so that time is be allowed for policy changes that may prevent the

crisis. However, the time horizon of private sector models is shorter and in this

situation, the criterion for evaluation of the accuracy of predictions (frequently,

a trading rule) is sometimes different (Berg et al., 2003). Most previous

researchers have preferred to utilize 24 months for their prediction horizon.

55

Using a two by two matrix in Table 4.1, the signal of each indicator can be

divided into four categories depending on its capability to predict the crisis

within the prediction horizon or signaling window.

TABLE 4.1 Performance Matrix of Early Warning Indicator

Within Signaling Horizon (w)

Crisis (cct=1) No Crisis (cct =0)

Signal (sit=1) A B

No signal (sit=0) C D

In Table 4.1, the first row of the 2x2 matrix indicates that the indicator sends a

signal (sit=1) as it exceeds its threshold. There are two possible categories, the

first, if the indicator issues a signal (sit=1) and a crisis happens within the

signaling horizon (cct=1), then this can be categorized as a “good signal” (cell

A); and the second, if the indicator sends a signal (sit=1) but no crisis occurs

within the crisis window (cct=0), it can be classified as a “false signal” (cell B).

On the other hand, the second row of a 2x2 matrix in Table 4.1 indicates that

even though the indicator failed to send a signal (sit=0) within its prediction

horizon, as it does not pass its threshold, there remain two possible categories:

the first category, if the indicator does not send a signal, but a crisis occurs

within the crisis window which can be considered as a “missed signal” (cell C).

In the other category, if the indicator does not send a signal and no crisis occurs

within the crisis window, it is called a “good silent signal” (cell D).

The signal model selects the set of independent variables based on their

performance in predicting past crises. The performance of an indicator in

predicting a crisis can be shown in the value of its noise-to-signal ratio (NSR).

Basically, the NSR is based on the ability of an indicator to send more good

signals, while at the same time eschewing bad signals. This ratio can be

obtained by taking the ratio of the percentage of bad signals over the

percentage of good signals (Kaminsky et al. 1998) or

56

�� = � �� + ��

� � + �� 4.1�

Correspondingly, the lower value of NSR reflects a more powerful leading

indicator in predicting crises. Therefore, based on this equation, if its NSR is

equal to or higher than one (NSR ≥ 1), its means that this indicator sends

excessive noise because its percentage of bad signals is equal to, or much bigger

than its percentage of good signals. As a result, it contributes less in predicting

crises compared to an indicator with low NSR. Kaminsky (1999) also argues

that the NSR can be used to select which indicators to use when constructing

the composite index.

4.3.2. Constructing a Composite Index

In the previous subsection it was noted that the signal model is a univariate

model, so that after selecting a set of leading indicators, the next step is to

integrate them into one single indicator or composite index. The purpose of

developing a composite index of a currency crisis is to aggregate the “best”

indicator (Goldstein et al., 2000; Krznar, 2004). By combining them into a well-

constructed composite index, the noise of these indicators can be reduced and

the composite index made smoother and more reliable for predicting a crisis.

With these points in mind, a combination of these selected indicators is

converted into one composite index by taking into account their performance,

placing more weight on the best indicators. In this respect, when constructing a

composite index (CIt), Kaminsky (1999) and Goldstein et al. (2000) weighted all

the signals (sit) of all leading indicators by the inverse of its NSR:

�� = � ��

�

��4.2��

Therefore, in constructing this composite index, the indicators with low NSR

receive more weight compared to those with high NSR.

57

4.3.3. Generating the Probability of a Currency Crisis

The composite index described above cannot be employed to predict a crisis

because it is unable to determine how big a chance a country has of

experiencing a crisis within 24 months. It can, only infer the likelihood that a

country will experience a crisis, for the higher its value, the more likely a

country will be beset with a crisis. So, in order to predict a crisis, this composite

index needs to be transformed into the probability of a crisis. For this purpose,

the composite index is then classified into several intervals using the decile

method. Furthermore, the probability of a crisis for each interval of the

composite index can be calculated using the following formula1:

�� !��"� < �� < ��$�% = &'. '(�)'&*ℎ��, *ℎ��"� < �� < ��$��-&.�-�/� � ��('00', &1�, *ℎ &�,�)'&*ℎ��&'. '(�)'&*ℎ��, *ℎ��"� < �� < ��$�

��4.3�

Where

Pg(Crisis│CIL < CIt < CIU) : The Probability of a crisis for CI ∈ g;

g : the number of intervals

Furthermore, by using the results from the above equation, the composite index

will be converted into the probability of a crisis according to the range

encompassing the composite index values.

4.3.4. Model Performance Evaluation

Unlike the parametric model, that is the probit/logit model, the forecasting

result of the signal model does not display if it is statistically significant or not,

because it does not involve hypothesis testing. Nonetheless, the predictive

power of the signal method can be evaluated in respect to its accuracy and

calibration. In evaluating the forecasting results of this model, this study applies

the Diebold and Rudebusch’s (1989) quadratic probability score (QPS) and

1 This formula is used by Kaminsky (1999); Berg and Pattillo (1999b); Edison (2000)); Zhuang and Dowling (2002); Yap (1998); Knedlik (2006)

58

global score bias (GSB) to evaluate the model’s performance in terms of its

accuracy and calibration2.

These methods allow evaluation of the average closeness between the

occurrence of a crisis and the prediction of a crisis during the pre-crisis

window. The occurrence crisis is determined using Equation 3.2 in Chapter 3.

Recall that this is a one-zero dummy model were the predicted crisis (Pt) is 1 for

“crisis” or 0 for “no crisis” and is defined if the model’s probability of a crisis

passes its threshold, that is, its specific cut-off probability. The accuracy of the

model can be assessed using the following formula:

3�� = 14 � 2�� − ��6

7

��4.4�

Where Pt : the predicted crisis periods with “1” for crisis and “0” for no

crisis;

Rt : the actual crises periods with “1” for crisis and “0” for no

crisis;

T : the sample period

GSB evaluates the closeness of the mean of the model’s forecasting probability

to the observed relative frequencies (Diebold and Rudebusch, 1989, Kaminsky,

1999) using the following formula:

8�� = 2��9 − �9�6��4.5�

where

�9 = 14 � ��

7

��4.6�

2 According to Diebold and Rudebusch (1989), the accuracy refers to the average closeness of predicted probabilities with the realization, as measured by a zero-one dummy variable. Similarly, the calibration is the closeness of predicted probabilities to observed relative frequencies (Zarnowitz, 1992).

59

�9 = 14 � ��

7

��4.7�

The score of both QPS and GSB ranges from 0 to 2 where a value of 0 represents

perfect accuracy or calibration and two represent perfect failure. This is because

when the model’s prediction and the actual value are the same, there is no

difference between them, or their gap is zero, or Pt = Rt. On the other hand,

when the model totally fails to predict the actual crisis periods, the difference

between them is 1 or -1. In the above equations, the square of this value keeps it

as a positive one. When multiplied by two and the predicted model totally fails

to predict the actual crises, the value of QPS or GSB is two. In other words, its

value being equal to two corresponds to “totally fail to predict”.

According to Berg and Pattillo (1999a), in addition to the above evaluation

method, the performance of the signal model can also be evaluated in terms of:

the percentage observation correctly called; the percentage of pre-crises period

correctly called; and the percentage of tranquil period correctly called. Using

the 2x2 performance matrix in Table 4.1, the crisis signal can be classified into

four categories, such as “A”, “B”, “C” and “D”. The performance of this model

can then be evaluated using the following assessment methods:

The percentage of observations correctly called = (A+D)/(A+B+C+D) (4.8)

The percentage of pre-crisis periods correctly called = A/(A+C) (4.9)

The percentage of tranquil periods correctly called = B/(B+D) (4.10)

The percentage of false alarms of total alarms = B/(A+B) (4.15)

Basically, for the first three measures and unlike the last measure, the higher

these values the better the performance of the model. In contrast, using the last

measure, the lower this ratio, the better the indicator will be.

60

4.4. The Application of General Signal EWS Model for Predicting

Indonesian Currency Crises

4.4.1. Constructing the Signal EWS Model

After explaining the EWS method, this section applies the EWS model based on

the signal model for predicting a currency crisis occurring in Indonesia. For this

purpose, in constructing the signal EWS model, the sample period chosen

ranges from January 1970 to September 2008. As the main purpose of this study

is to develop an EWS model that can predict the Asian financial crisis in

1997/98 within 24 months, the data will be divided into two sub-samples, these

being in-sample and out-of-sample. The in-sample extends from January 1970

to December 1995, and will be used for model building. The out-of-sample goes

from January 1996 to September 2008, and will be used to test the performance

of the EWS model in predicting the Asian Financial Crisis in 1997/98.

Since the determination of the period of currency crises in Indonesia has been

done in previous chapter (Chapter 3), in this section, to construct the Signal

EWS model begins by selecting the set of leading indicators for constructing the

composite index. In common with Kaminsky et al. (1998) and Eliasson and

Kreuter (2001) who used a broader set of leading indicators, this study uses 55

leading indicators that are classified into six categories: the capital account, the

current account, the financial sector, the fiscal sector, the real sector, and the

global economy, as presented in Table A4.1.

As a first step, the behaviour of these 55 monthly leading indicators is

transformed into a binary signal, which is “1” for “signal” if its value passes its

threshold,3 or otherwise, “0” for “no signal”. All these signals from the 55

indicators are then classified according to their ability to predict the in-sample

currency crises (November 1978, April 1983 and September 1986) within the 24-

3 Similar to calculating the thresholds for EMPI in Equation 3.2, the threshold for each leading indicator

is calculated using mean +/- specific number of standard deviation. The value of this specific number

varies across the leading indicators and is adjusted individually until its NSR reaches a minimum level.

61

month crisis window, with reference to the 2x2 performance matrix in Table 4.1.

These leading indicators are then evaluated individually and ranked on their

ability to send more good signals, at the same time the results of keeping on

sending weaker signals using NSR are presented in Table 4.2. Based on this

assessment, ten indicators were found with NSR equal to or greater than unity

(NSR ≥ 1), and six indicators with no NSR, as they failed to send out any good

signals. Following Kaminsky (1999), these indicators were then dropped from

the model because they sent more noise, or played a lesser part in predicting a

crisis. Consequently, only 39 of 55 leading indicators are used to construct a

composite index.

TABLE 4.2 The Performance Evaluation of Individual Indicators

No Leading Indicators A/(A+C) B/(B+D) NSR

01 Real US$/yen exchange rate1 14.47% 0.43% 0.03

02 Short-term capital flows to GDP 13.16% 0.45% 0.03

03 Current account balance to GDP 21.15% 0.79% 0.04

04 US annual growth rate 14.47% 0.85% 0.06

05 US real interest rate3 5.26% 0.45% 0.09

06 Short-term capital flows to GDP3 3.95% 0.47% 0.12

07 US real interest rate 22.37% 2.98% 0.13

08 Loans to deposits3 15.79% 3.59% 0.23

09 M1 to GDP3 11.84% 2.69% 0.23

10 Real effective exchange rate1 59.21% 14.04% 0.24

11 Domestic real interest rate3 100.00% 26.47% 0.26

12 Exports2 28.95% 8.52% 0.29

13 M1 to GDP 6.58% 2.13% 0.32

14 Government consumption to GDP 85.53% 28.51% 0.33

15 Foreign reserves in months of imports 2.63% 0.89% 0.34

16 Trade balance to GDP3 6.58% 2.24% 0.34

17 Foreign reserves2 15.79% 5.38% 0.34

18 Foreign reserves in months of imports3 26.32% 9.43% 0.36

19 Government consumption to GDP, 12 m change 63.16% 23.77% 0.38

20 Domestic real interest rate differential from US rate3

75.00% 28.43% 0.38

21 Lending ]deposit rate spread 54.55% 21.50% 0.39

22 Current account balance to GDP3 16.28% 6.45% 0.40

23 Deposits to M23 7.89% 3.14% 0.40

24 Net credit to government to GDP3 26.92% 11.02% 0.41

25 Fiscal balance to GDP3 42.11% 17.94% 0.43

26 Deposits in BIS banks to reserves3 100.00% 44.44% 0.44

27 Real exchange rate against US$1 30.26% 15.74% 0.52

28 Domestic real interest rate 36.36% 20.56% 0.57

62

TABLE 4.2 The Performance Evaluation of Individual Indicators (Continued)

No Leading Indicators A/(A+C) B/(B+D) NSR

29 M2 to reserves3 13.16% 7.62% 0.58

30 Fiscal balance to GDP 27.63% 16.60% 0.60

31 M2 multiplier2 65.79% 40.81% 0.62

32 M2 multiplier 48.68% 30.21% 0.62

33 Oil price 82.89% 51.49% 0.62

34 M2 to reserves 1.32% 0.85% 0.65

35 Domestic credit to GDP3 39.47% 28.70% 0.73

36 Central bank credit to the public sector to GDP 31.58% 23.40% 0.74

37 Domestic real interest rate differential from US rate

27.27% 22.43% 0.82

38 Real commercial bank deposits2 76.32% 72.20% 0.95

39 Trade balance to GDP 35.53% 35.32% 0.99

40 Short-term external debt to reserves3 52.63% 52.47% 1.00

41 Foreign liabilities to foreign assets3 44.74% 58.30% 1.30

42 Domestic credit to GDP 57.89% 78.30% 1.35

43 Net credit to government to GDP 50.00% 82.01% 1.64

44 Central bank credit to the public sector to GDP3 13.16% 21.97% 1.67

45 Short-term external debt to reserves 5.26% 8.94% 1.70

46 Imports2 13.16% 24.22% 1.84

47 Stock price index in local currency2 25.00% 48.67% 1.95

48 Foreign liabilities to foreign assets 5.26% 20.85% 3.96

49 Loans to deposits 5.26% 32.34% 6.14

50 Deposits in BIS banks to reserves 0.00% 9.09% NA

51 Deposits to M2 0.00% 1.28% NA

52 Lending-deposit rate spread3 0.00% 42.16% NA

53 Oil price2 0.00% 2.69% NA

54 Industrial/manufacturing production index2 0.00% 0.00% NA

55 Domestic consumer price index2 0.00% 12.11% NA

Note: 1 deviation from trend-HP filter, 2 12 months percentage change, 3 12 months change, NA not available.

The next step is to integrate all signals from these 39 indicators into one

composite index. According to Kaminsky (1999), the composite index can be

obtained by summing up all signals from these indicators. This method

assumes that the predictive power is the same over all the indicators. In fact,

based on the value of NSR, which is listed in Table 4.2, the predictive power of

each indicator is different. In respect to this situation, Kaminsky (1999) and

Goldstein et al. (2000) propose an alternative way to construct the composite

index by employing a weight of the inverse of the minimum adjusted noise-to-

signal-ratio on each signal mentioned in Equation 4.2. This means that

indicators with the lower NSR will contribute more to develop a composite

63

index compared to indicators with a higher noise-to-signal-ratio. For example,

the real US$/yen exchange rate, with NSR of 0.03, based on the calculation of

Equation 4.2, has a weight of 33.33. In contrast, the trade balance to GDP of the

NSR is 0.99, and using the same method, it will be given a weight of 1.01. The

time-series composite index is presented in Figure 4.1.

FIGURE 4.1 Composites Index, 1970-2008

The composite index can only infer the likelihood that a country will experience

a crisis, as the higher its value, the more likely a country will be beset with a

crisis. In this study, the value of the composite index ranges from 0 to 120.6;

therefore it is difficult to interpret the meaning of an individual value in the

composite index. To attempt to clarify this situation, the composite index is

converted to a measure of the probability of a crisis occurring within the next 24

months. For this purpose, the composite index is classified into ten intervals

using the decile method; however, based on the calculations, it was found that

several intervals had the same lower and upper bounds - thus this study used

only eight intervals. The probability of a crisis for each interval of the composite

index can then be calculated using Equation 4.3, the result of which is shown in

Table 4.3.

0

25

50

75

100

125

Jan-70

Jan-71

Jan-72

Jan-73

Jan-74

Jan-75

Jan-76

Jan-77

Jan-78

Jan-79

Jan-80

Jan-81

Jan-82

Jan-83

Jan-84

Jan-85

Jan-86

Jan-87

Jan-88

Jan-89

Jan-90

Jan-91

Jan-92

Jan-93

Jan-94

Jan-95

Jan-96

Jan-97

Jan-98

Jan-99

Jan-00

Jan-01

Jan-02

Jan-03

Jan-04

Jan-05

Jan-06

Jan-07

Jan-08

64

TABLE 4.3 Composite Index and Probabilities of a Currency Crisis

No Deciles Range of Composite Index Probability of a crisis

1 0 - 2nd 0.0 < CI ≤ 7.9 0%

2 2nd – 4th 7.9 < CI ≤ 11.6 3.2%

3 4th – 5th 11.6 < CI ≤ 14.0 6.5%

4 5th – 6th 14.0 < CI ≤ 16.8 12.5%

5 6th – 7th 16.8 < CI ≤ 19.5 35.5%

6 7th – 8th 19.5 < CI ≤ 23.9 37.5%

7 8th – 9th 23.9 < CI ≤ 33.1 51.7%

8 9th - 10th 33.1 < CI ≤ 120.6 83.9%

With reference to Table 4.3, each composite index for all samples periods is

converted into the probability of a crisis that corresponds according to which

interval of composite index it falls. For example, if the composite index at time t

is 5, then based on the above table, it will go into the first interval and will be

converted to the probability of a crisis with the value of 0%. However, if the

value of the composite index at time t is equal to 20, then it falls into the sixth

interval and the probability of a crisis becomes 37.5%. The time-series

probability of a crisis is presented in Figures 4.2 and 4.3.

4.4.2. Predicting Indonesian Currency Crises

In this subsection, this signal model is used to predict and evaluate its ability to

predict the Indonesian currency crises, as presented in Figure 4.2 for the in-

sample prediction (1970-1995), and Figure 4.3 for the out-of-sample prediction

(1996-2008). In these figures, the red solid line is the probability of a crisis. As

already mentioned in the previous chapter, Indonesia experienced four

episodes of currency crisis, being three during the in-sample period 1970-1995

(November 1978, April 1983 and September 1986), and one currency crisis

episode in the out-of-sample period 1996-1998, that being the Asian financial

crisis of 1997/98. As this study uses the 24-month of crisis window, the yellow

shaded areas (cc_24m) represent the 24 months prior to the currency crises.

65

In-Sample Prediction (1970-1995)

Using Figure 4.2 to predict the three in-sample currency crises between 1970–

1995, it was found that this model was able to predict all three because the

probability of a crisis tended to increase within 24 months prior to these events.

For example, in predicting the first episode of a currency crisis in Indonesia that

occurred in November 1978, the model sent signals about the potential currency

crisis from December 1976, when the probability of a crisis was about 36%. This

probability increased to 52% in January 1977 and jumped to 84% in January

1978 where it remained until the crisis happened in November 1978.

In the second in-sample crisis that happened in April 1983, the model started by

sending a signal from May 1981 when the probability of a crisis happening

within 24 months was about 52%. This continued to increase to 84% in June

1981, or almost 19 months prior to the crisis. Unlike the first two crisis episodes,

in the third crisis that happened in September 1986, the first signal for the

occurrence appeared in October 1984 at about 13%, and then increased to 84%

from January to March 1985, before declining to 38% in June 1985. But in March

1986, or six months prior to the crisis, the probability of a crisis jumped to 52%.

Although this model was able to capture all the in-sample currency crises in

Indonesia, it also sent some false alarms, especially in the early 1970s and 1990s.

For example, the model’s probability of a crisis increased from 13% in January

1973 to 52% in January 1974. One year later, the model indicated that Indonesia

would be hit by a crisis as the probability of a crisis increased from 36% in

January 1975 to 52% in March 1975 and remained high until December 1975. In

the early 1990s, the model’s probability of a crisis tended to fluctuate but

reached a peak of 52% in November 1992.

FIGURE 4.2 The General Signal Model: I

During these periods Indonesia also experienced several major ev

the large demonstration of students

Kukui Tanaka, Prime Minister of Japan

anti-Japan/China riots known as “Malari” (Indonesian acronym for Malapetaka

lima belas Januari, or 15 January’s disaster). This riot

government to apply new foreign capital regulation to localize the ownership of

foreign companies and to restrict

(Sato, 2003). In addition, at th

between Indonesia and East Timor (now, Timor Leste). Moreover,

1992, there was an episode involving a

liquidated Bank Summa International, one of the te

Indonesia.

Out-of-Sample Prediction (1996

To test the performance of this signal

currency crises, Figure 4.3 shows

to 2008. This model is evaluated

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%1

97

0M

01

19

70

M1

2

19

71

M1

1

19

72

M1

0

19

73

M0

9

19

74

M0

8

19

75

M0

7

19

76

M0

6

19

77

M0

5

19

78

M0

4

66

eneral Signal Model: In-Sample Prediction

Indonesia also experienced several major events, such as

the large demonstration of students that took place on arrival in Jakarta

Kukui Tanaka, Prime Minister of Japan on 15 January 1974, which sparked the

known as “Malari” (Indonesian acronym for Malapetaka

or 15 January’s disaster). This riot was a turning point for


foreign companies and to restrict the use of foreign employees in Indonesia

the end of 1975, there was a military confrontation

between Indonesia and East Timor (now, Timor Leste). Moreover, at the end of

involving a “mini banking crisis” as Bank Indonesia

liquidated Bank Summa International, one of the ten largest private

(1996-2008)

To test the performance of this signal model in predicting the out-of

shows the time series probability of a crisis from 1996

model is evaluated here in terms of its ability to predict the

19

78

M0

4

19

79

M0

3

19

80

M0

2

19

81

M0

1

19

81

M1

2

19

82

M1

1

19

83

M1

0

19

84

M0

9

19

85

M0

8

19

86

M0

7

19

87

M0

6

19

88

M0

5

19

89

M0

4

19

90

M0

3

19

91

M0

2

19

92

M0

1

19

92

M1

2

cc24m signal

ents, such as

in Jakarta of Mr.

which sparked the

known as “Malari” (Indonesian acronym for Malapetaka

a turning point for the


in Indonesia

e end of 1975, there was a military confrontation

the end of

Bank Indonesia

private banks in

of-sample

from 1996

of its ability to predict the

19

92

M1

2

19

93

M1

1

19

94

M1

0

19

95

M0

9

1997/98 Asian financial crisis

this period.

FIGURE 4.

Based on this figure, warning signals

from April 1996 when

crisis was about 36%

before dropping to 3

increased significantly and reach

seven months prior to the

financial crisis in August 1997

decline to 7% in July

remaining high for

probability of a crisis decline significantly to 38%

September 1998.

To see whether this model could capture any potential risks that occurred in the

Indonesian economy during this period

January 1999 to September 2008. As seen in Figure 4.

signal model sent

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%1

99

6M

01

19

96

M0

7

19

97

M0

1

19

97

M0

7

19

98

M0

1

67

1997/98 Asian financial crisis, this being the only currency crisis found during

FIGURE 4.3 The General Signal Model: Out-of-Sample

Based on this figure, warning signals began to show the presence of this crisis

when the probability of Indonesia experiencing a

about 36%. This figure increased to 38% in the following

to 3% in August 1996. After this, the probabilit

significantly and reached a high of 84% in January 1997

seven months prior to the first time EMPI crossed its threshold for the Asian

in August 1997. After that the probability of

July 1997, before increasing again to 84% in January 1998

high for another three months. However, after

crisis decline significantly to 38% and continue


Indonesian economy during this period the sample period

January 1999 to September 2008. As seen in Figure 4.3, during this period

some warning signals that indicated Indonesia would be

19

98

M0

1

19

98

M0

7

19

99

M0

1

19

99

M0

7

20

00

M0

1

20

00

M0

7

20

01

M0

1

20

01

M0

7

20

02

M0

1

20

02

M0

7

20

03

M0

1

20

03

M0

7

20

04

M0

1

20

04

M0

7

20

05

M0

1

20

05

M0

7

cc24m signal

the only currency crisis found during

ample Prediction

presence of this crisis

experiencing a currency

to 38% in the following month

the probability of a crisis

in January 1997, that is, some

its threshold for the Asian

probability of a crisis tended to

84% in January 1998,

after April 1998, the

and continued drop to 13% in


the sample period was extended from

during this period, the

warning signals that indicated Indonesia would be

20

05

M0

7

20

06

M0

1

20

06

M0

7

20

07

M0

1

20

07

M0

7

20

08

M0

1

20

08

M0

7

68

plagued by crises within the 24-month crisis window. For example, the model

started to send warning signals from January 1999 with the probability of a

crisis being 35.5%. This continued to increase to 52% in June 1999, which

indicates that the chance of Indonesia experiencing a crisis within 24 months

onwards was about 52%. The model’s probability of a crisis remained at 52%

until March 2000 before decreasing to 12.5%. However, the probability of a

crisis increased again to 52% in January 2001 and peaked at 84% in August

2001.

After October 2003, the model began to send warning signals with an average

probability of around 35%, and finally, warning signals from January 2008

indicated the probability of a currency crisis happening within 24 months was

52%. The crisis probability remained high to peak at 84% in September 2008.

However, as the purpose of this study is to develop the EWS model to predict

currency crises, and as the only out-of-sample currency crisis was the Asian

Financial Crisis in 1997/98, these warning signals can be seen as false alarms or

false signals.

In fact, Indonesia experienced several vulnerabilities during the period from

1999 to 2008, and significantly these events occurred within 24 months after the

model’s false alarms. For example, after the first alarm in the out-of-sample

period, there was political instability that led the People's Consultative

Assembly (Majelis Permusyawaratan Rakyat, MPR) to impeach the fourth

President of Republic of Indonesia, Abdurrahman Wahid. Then on 23 July 2001,

Wahid was replaced by Megawati Sukarnoputri as the fifth President. In

relation to the second false alarm, within 24 months following the first signal in

October 2003, Indonesia experienced economic problems triggered by increases

in the world oil price. This forced the Indonesian government to remove

substantial subsidies on domestic fuel in May and October 2005. As a result, the

domestic oil price climbed significantly and the inflation rate increased to 17%.

Lastly, associated with the third false alarm, which started in January 2008,

there was a global financial crisis that was derived from the sub-prime

69

mortgage crisis in the United States of America, which in turn affected many

countries around the world, including Indonesia.

4.4.3 The Signal Model’s Performance Evaluation

In evaluating the reliability of the model in predicting these above crisis

episodes, as mentioned earlier that the probability of a crisis need to be

converted into one-zero dummy model predicted crises using cut-off

probabilities as the threshold. Following that, this study sets up four cut-off

probabilities (Pr*), namely 20%, 30%, 40% and 50%. It means that the model will

indicate a currency crisis if the probability of a crisis crosses these cut-off

probabilities. The in-sample performance is evaluated based on the ability of

the model to predict the three in-sample currency crises, while the out-of-

sample performance evaluation is based on the ability to predict the 1997/98

Asian financial crisis. Details of the model’s performance evaluation for both in-

sample and out-of-sample currency crises can be seen in Table 4.4.

With the cut-off probability of 20% and 30%, the model correctly figures out the

overall crisis episodes at about 83% for in-sample crises and 73% for the out-of-

sample crises. However, when Pr* increases to 50%, the ability of the models to

predict 24 months prior to the crisis fell to 57% (in-sample) and 30% (out-of-

sample). In contrast, in capturing the tranquil periods, this model performs well

in line with the increase in Pr*. For example, at Pr*=20%, this model correctly

captures the tranquil period by 73% (in-sample) and 36% (out-of-sample),

however, as Pr* increases to 50%, the ability of this model also increases to 92%

and to 76%, respectively.

Generally, the performance of this signal model can also be seen from the

ability of the model to capture all observation including crisis and tranquil

periods. As shown in Table 4.4, at Pr*=20%, this model can capture 77% (in-

sample) and 43% (out-of-sample). The ability of this model increases in line

with increasing Pr*, for example when Pr*=50%, its performance increases to

83% (in-sample) and 67% (out-of-sample). In contrast, as Pr* increases the ability

70

of the model to predict the whole observation during the crisis period from

1996 to 1998 drops, so that at Pr*=20%, this model captures the whole period by

78%, although it declines to 42% as Pr* increases to 50%. Moreover, in

predicting these currency crises, the performance of this model can also be

evaluated in term of its accuracy and calibration using the ratio of QPS and

GSB, as mentioned in Table 4.4. According to these measurements, in general,

this model performs better than is indicated by its low QPS and GSB for both

in-sample and out-of-sample.

TABLE 4.4 The General Signal Model’s Performance Evaluations

Thresholds (Pr*)

Assessment methods In-sample Out-of-sample

1970-1995 1996-1998 1996-2008

20%

% of observation correctly called 76.53% 77.78% 43.14%

% of pre-crisis periods correctly called 82.90% 73.33% 73.33%

% of tranquil periods correctly called 74.47% 100.00% 35.77%

% of false alarms of total alarms 48.78% 0.00% 78.22%

QPS 0.4695 0.4444 1.1373

GSB 0.0457 0.0988 0.4307

30%





QPS 0.4695 0.4444 1.1373

GSB 0.0457 0.0988 0.4307

40%





QPS 0.3344 1.1667 0.6667

GSB 0.0041 0.6806 0.0069

50%





QPS 0.3344 1.1667 0.6667

GSB 0.0041 0.6806 0.0069

As noted in the previous subsection, based on Figures 4.2 and 4.3 this model

sends lots of false alarms for both in-samples and out-of-samples. It is also

supported by the percentage of false signals relative to the total signals in Table

4.4. According to this measure, during the period 1970 to 1995, this model sent

71

49% of false alarms at Pr*=20%. However as Pr*is increased, the number of false

alarms tended to decline, for example, at Pr*=50%, the number of in-sample

false alarms declines to 31%. However, during the crisis period from 1996 to

1998, the model sent no false alarms at all levels of cut-off probabilities. In

contrast, for the entire out-of-sample period from 1996 to 2008, this model sent

many false alarms, for example, at Pr*=20% it sent 78% of false alarms, as Pr*

increased to 50%, its percentage of false alarms relative to total signals dropped

slightly to 77%.

4.5. Assessing Sector Specific Forecasting and the Crisis Channels

In the previous section it was pointed out that this study uses an extensive data

set representing 55 indicators. These indicators can be classified into six sectors,

namely the capital account, the current account, the financial sector, the fiscal

account, the global economy and the real sector. In this section, the analysis is

extended by employing the sector-specific signal model to identify which sector

is the most useful for forecasting currency vulnerability and explaining the

source of these currency crises. For this purpose, using the same method and

assumptions as the general model, the probability of a crisis based on

modification from each sector is calculated. In addition, to evaluate the

performance of these sector-specific models, the 30% cut-off probability is used,

as the previous section indicates that this threshold is the optimal cut-off

probability which performs very well with the general model in predicting both

in-sample and out-of-sample Indonesian currency crises.

4.5.1. Capital Account Sector Specific Signal EWS Model

In developing the sectoral model for the capital account, this sector used 11

from 15 leading indicators. Three indicators have their NSR greater than unity,

while the other indicators have no NSR due to the inability to send a good

signal, as can be seen in Table A4.2. Using the same approach with the general

model, the probability of a crisis for this sector is calculated, and Figure 4.4

72

presents the time-series of the probability of a crisis for both in-sample and out-

of-sample periods.

In that figure, the probability of a crisis varies from 0 to 77%. For the in-sample

performance this sector was able to predict two of three in-sample crises as

precisely November 1978 and April 1983, but it was unable to predict the third

in-sample crisis in September 1986. The first indication for the crisis of 1978 is

seen in January 1978 when the probability of a crisis reached 77%, where it

remained until the crisis occurred. In the second crisis episode, during the first

24 months prior to the crisis of May 1981, the indication for a crisis to occur was

about 20%. This tended to increase closer to the crisis date and reached 77% in

June 1982, or ten months before the crisis took place in April 1983.

FIGURE 4.4 Probability of a Crisis for the Capital Account, 1970-2008

In contrast, this sector failed to predict the last in-sample crisis episode in

September 1986 because its probability of a crisis only reached 20% on the first

two of the 24-months crisis windows. After this it disappeared, returning again

to 20% in December 1985 where it remained until the crisis happened in

September 1986. Moreover, during this period, it also generated several false

signals particularly in the early 1970s and 1990s.

0%

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100%

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cc_24m KA

73

At the outset, the out-of-sample performance in Figure 4.4 shows that this

sector sent weak alarm signals about the occurrence of the Asian financial crisis,

with the crisis probability of about 7% in March 1996, increasing to 16% in

August 1996 before shifting back to 0% in January 1997. After that, its crisis

probability fluctuates around 0% to 16%; however, after the Indonesian

government closed 16 small banks in November 1997, the probability of a crisis

jumped to 77% in December 1997 and remained there consistently until March

1998, before moving back to 7% in May 1998 where it remained until the end of

crisis window.

As the sample period was extended, this sector still sent warning signals, as its

probability of a crisis increased during this period. For example, a significant

alarm occurred in August 2001, as well as from July to October 2005, when its

probability of a crisis was about 77%. Indonesia also experienced two major

events during this period, namely the impeachment of the fourth President of

Republic of Indonesia in July 2001, and in May and October 2005, the

Indonesian government implemented the policy on substantial removal of the

oil subsidy by increasing the domestic oil price. However, as the purpose of this

model is to predict currency crises, these warning signals can be categorized as

false alarms.

4.5.2. Current Account Sector Specific Signal EWS Model

In constructing its composite index as well as the probability of a crisis, this

sector only uses 7 of 8 indicators as it drops the 12-month change (yoy) of

imports because its NSR is greater than unity; see Table A.4.3. Figure 4.5 shows

that the current account signal model can predict all the crisis episodes, both in-

sample and out-of-sample. In the first crisis in November 1978, warning signals

were received about the possibility of the crisis happening within 24 months.

This was about 46%, a figure that had increased to 73% in November 1977

where it remained for another three months before shifting back to 43%.

74

For the second in-sample crisis episode, the same situation applied, as the

probability of a crisis reached 73% some 24 months prior to this crisis. After

that, it tended to fluctuate with an average probability of about 68%. However,

following January 1983 its probability of a crisis remained at 73% until the date

of the crisis. In the last in-sample evaluation, 24 months prior to the crisis, the

probability of a crisis jumped to 73% before returning to around 46% for the rest

of the period until the crisis time in September 1986, except in June 1985 and

March 1986, when 73% was recorded. However, during this in-sample period,

this sector also sent lots of false alarms, particularly at the beginning of 1970s.

FIGURE 4.5 Probability of a Crisis of the Current Account, 1970-2008

In the out-of-sample, the first indication of Asian financial crisis can be seen in

April 1996 when the probability of a crisis was 46%, a figure that increased to

73% in the following month. After this it tends to fluctuate around that value

before dropping to 0% from July to September 1997, and fluctuates around 0%

to 18% for the rest of crisis window. Moreover, as the sample period has been

extended to 2008, this sector still sends lots of warning signals with the

probability of a crisis reaching 43 to 46% during 1999. A peak at 73% was

reached in June 1999, from 2001 to 2003, and from August to November 2004.

0%

10%

20%

30%

40%

50%

60%

70%

80%

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100%

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cc_24m CA

75

Similarly, after January 2006, its probability of a crisis reached 43% until the end

of 2007, before jumping to 73% in 2008. As this study sets 30% as the cut-off

probability, these warning signals indicate that Indonesia would be hit by a

crisis and the probability for Indonesia having a crisis within 24 months in 2008

was 77%. Even though Indonesia experienced some vulnerability in this period,

no currency crisis did occur, thus, the warning signals can be categorized as

false alarms.

4.5.3. Financial Sector Specific Signal EWS Model

This sector only utilizes 11 of 15 potential leading indicators to construct the

composite index because their NSR is less than unity as seen in Table A.4.4. The

sector’s probability of a crisis is presented in Figure 4.6. The in-sample

prediction indicates that the financial sector only sent five significant warning

signals with the probability of a crisis reaching 60% in May 1971, January 1984,

and three times during the 24 months prior to the third in-sample crisis, in

February and August 1985, and January 1986. For the rest of sample periods its

probability of a crisis tended to fluctuate between 0 to 33%, though it sometimes

reached 39%.

FIGURE 4.6 Probability of a Crisis for the Financial Sector, 1970-2008

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

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cc_24m FS

76

For the first two in-sample crises, warning signals were sent with a 32%

probability of a crisis. Setting the 30% cut-off probability as the threshold to

define the crises, the currency crisis is determined whenever the model’s crisis

probability crosses this threshold. In Figure 4.6 it is shown that this sector is

able to capture all the in-sample currency crises with the probability of a crisis

fluctuating around 33 to 39%. On the other hand, the ability of this model to

capture the tranquil periods is limited, as noted by the large number of false

alarms sent during this period.

Similarly, when predicting the out-of-sample currency crises, warning signals

were sent from January 1996 with the probability of a crisis at 32%, further

increasing to 39% after January 1997 for almost the rest of the crisis window,

before going back to 32% in January 1998. As the sample has been extended to

the end of 2008, this figure shows that this sector sent many false signals during

this period, particularly during the impeachment of Abdurahman Wahid as the

fourth President of Republic of Indonesia in July 2001, during the episode of

mini crisis in 2005, as well as the presence of the Global Financial Crisis in 2008.

4.5.4. Fiscal Account Sector Specific Signal EWS Model

In this section a EWS model using 6 of 8 leading indicators is developed, see

Table A.4.5. The probability of a crisis for this section is presented in Figure 4.7.

Based on this figure, in the first in-sample crisis, warning signals were sent from

48% at the beginning of a 24 month-crisis window to 54% from January 1978

until the crisis date. In the next two in-sample crises, the fiscal account provides

the probability of having a currency crisis of 86%.

On the other hand, in the out-of-sample, there was a failure to predict the Asian

financial crisis, as its probability of a crisis remained lower for the whole a 24

month-crisis window, reaching only 21%, or less than the selected cut-off

probability of 30%. However, the probability of a crisis increased substantially

to 84% in September 1998 for about four months before going back to 54% until

January 2000 and falling even further to 18% in the following month. The crisis

77

probability increased again to 54% from January 2002, and further increased to

86% in August 2003, where it remained for five months. It then dropped to 18%

until the end of sample period, with the exception of 2005 when the probability

of a crisis slightly increased to 21% for the entire year. But, as there was no

currency crisis occurring within 24 months after these warning signals, these

signals can be declared false alarms.

FIGURE 4.7 Probability of a Crisis for the Fiscal Accounts, 1970-2008

4.5.5. Global Economy Sector Specific Signal EWS Model

In developing the EWS model for this sector, this study uses 5 of 6 leading

indicators, with its probability of a crisis being presented in Figure 4.8. Based on

this figure, it was found that this sector was unable to predict the first in-sample

crisis in November 1978, as its crisis probability was only 18% for the whole

crisis window. On the other hand, in the next two in-sample crises, in April

1983 and September 1986, the presence of both crises can be identified, as the

probability of a crisis climbed to 82% within the 24-month crisis window.

However, unlike the second in-sample crisis, this percentage of probability for

the third in-sample crisis dropped substantially to 18% after April 1984 before

0%

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2007

1/1/

2008

cc_24m FA

78

further dropping to 0% after January 1986. During the in-sample period, less

false signals were sent, particularly in January 1974.

As with predicting the third in-sample crisis, when predicting the out-of-

sample currency crises, the probability results are low for almost the whole

crisis window. Warning signals are noted from March 1996 with probability of

a crisis being 18%, after which there is a jump to 82% from January to February

1997, before fluctuations between 0 to 40% are witnessed in the rest of the crisis

window. As the sample period is extended to September 2008, this sector sends

some false alarms as its crisis probability plummets before reaching to a peak of

82% for the whole of 2001, as well as the whole of 2008.

FIGURE 4.8 Probability of a Crisis for the Global Economy, 1970-2008

4.5.6. Real Sector Specific Signal EWS Model

Unfortunately, for this category, the study is unable to construct either the

composite index or the probability of a crisis because there are no indicators

with NSR less than unity.

0%

10%

20%

30%

40%

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90%

100%

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cc_24m GE

79

4.5.7. Performance Evaluation for Sector Specific Forecasting Results

As mentioned earlier in this section, this study uses the 30% cut-off probability

to evaluate the performance of these sector specific signal EWS models, so these

sectors can define currency crises if their probability of a crisis crosses this

threshold. As the focus of this study is to develop an EWS model to predict

currency crises, more attention is placed on the ability of this model to predict

currency crises. However, similar to the assessment of the general model in

Table 4.4 in the previous section, this section also includes the performance

assessment results from the other measurements in Table 4.5.

In terms of the ability to predict the 24 month pre-crisis period, this study found

that the financial sector performed very well, as it was able to capture 99% of

the 24-month in-sample pre-crisis period, followed by the current account, the

fiscal account, the global economy and the capital account, which were able to

capture 80%, 68%, 29% and 26%, respectively. Similarly, in predicting the out-

of-sample crisis, the financial sector also performed better than the other

sectors, as it was able to capture 90% of the pre-crisis period, followed by the

current account, the global economy and the capital account. On the other hand,

unlike the other sectors, the fiscal account failed to predict the out-of-sample

pre-crisis period, as it was unable to send any warning signals during this pre-

crisis period.

In this model, warning signals were generally generated based on their

behaviour before the onset of a crisis. These signals would be sent whenever the

movement of indicators crossed their thresholds. Thus, more vulnerable

indicators would send more signals. Similarly, in terms of sector-specific

signals, more vulnerable sectors would send more signals that would result in a

higher percentage of the pre-crisis period being correctly predicted, but would

reduce the percentage of tranquil periods correctly called because of the high

incidence of false alarms. This study found that compared to other sectors, the

domestic sector, particularly the financial sector, was the most vulnerable

sector. This finding is also supported by Zhuang and Dowling (2002), who

80

found that for Indonesia, domestic weaknesses were the main source of the

Asian financial crisis.

TABLE 4.5 Performance Evaluation of the Sector Specific Signal EWS Models*

Periods Assessment methods KA CA FS FA GE

In-sample

1970-95

% of observations correctly called 80.39% 78.14% 56.59% 82.64% 79.42%

% of pre-crisis periods correctly called 26.32% 80.26% 98.68% 68.42% 28.95%

% of tranquil periods correctly called 97.87% 77.45% 42.98% 87.23% 95.74%

% of false alarms of total alarms 20.00% 46.49% 64.11% 36.59% 31.25%

NSR 0.0489 0.2847 0.5930 0.2013 0.0832

QPS 0.3923 0.4373 0.8682 0.3473 0.4116

GSB 0.0538 0.0299 0.3658 0.0007 0.0400

Out-of-sample

1996-1998





NSR 0.0000 0.0000 0.3704 NA 0.0000

QPS 1.4444 0.8889 0.2778 1.8889 1.1667

GSB 1.0432 0.3951 0.0015 1.0432 0.6806

1996-2008





NSR 0.0488 0.6829 0.4797 NA 0.1707

QPS 0.4183 1.3072 0.8105 0.9281 0.5490

GSB 0.0342 0.0000 0.2679 0.0103 0.0000

Note: KA: capital account; CA: current account; FS: financial sector; FA: fiscal account; GE: global economy; NA: not available; * based on a 30% cut-off-probability.

In relation to predicting the three in-sample crisis episodes (November 1978,

April 1983 and September 1986); using the 30% cut-off probability, this study

found that only three sectors could predict these crises, namely the current

account, the financial sector and the fiscal sector. The fact that these three

sectors dominated the percentage of pre-crisis periods identified in Table 4.5

could help to support these finding. Moreover, for out-of-sample predictions,

two sectors, the fiscal account and real sector, did not send any warning signals

about the presence of the Asian Financial Crisis in 1997/98. This can be seen in

Figures 4.4 to 4.8, with the following Table 4.6 giving their prediction results for

all currency crises in Indonesia from 1970 to 2008.

81

TABLE 4.6 The Forecasting Results on Indonesian Currency Crises, 1970-2008

The Cut-off Probability is 30 %

Sector

Currency Crisis Period

November 1978

April 1983 September

1986 Asian Financial Crisis

in 1997/98

Overall √√√√ √√√√ √√√√ √√√√

Capital Account √√√√ √√√√ √√√√

Current Account √√√√ √√√√ √√√√ √√√√

Financial Sector √√√√ √√√√ √√√√ √√√√

Fiscal Account √√√√ √√√√ √√√√

Global Economy √√√√ √√√√ √√√√

4.6. Conclusions

This study has attempted to develop an early warning system model to predict

the episode of currency crises in Indonesia from January 1970 to September

2008. For this purpose, this chapter employed a signal approach EWS model to

predict the currency crises using 39 out of 55 monthly leading indicators from

six sectors, namely the capital account, the current account, the financial sector,

the fiscal account, the global economy and the real sector.

The findings indicate that the model correctly captured all crisis episodes for

both in-sample and out-sample periods. In addition, according to the validity

measures, the model correctly predicted 83% (for in-sample crises) and 73% (for

out-of-sample crises) at the 30% cut-off probability. However, the model also

sent many false signals during these periods.

In addition, the model points to domestic sector weaknesses as the main

underlying factor for Indonesian currency crises. In relation to sector specific

analysis, the financial sector is the most dominant sector compared to the other

five sectors. The model is also able to identify the most vulnerable sectors

because sending many false alarms limits its predictions for tranquil periods.

.

82

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ies

to f

ore

ign

ass

ets

12 m

ch

ang

e T

he

rati

o o

f F

ore

ign

lia

bil

itie

s o

f th

e b

ank

ing

sec

tor

(IF

S l

ine

26C

) to

Fo

reig

n a

sset

s o

f th

e b

ank

ing

sec

tor

(IF

S l

ine

26C

)

Fo

reig

n r

eser

ves

in

mo

nth

s o

f im

po

rts

R

atio

of

Fo

reig

n r

eser

ves

min

us

go

ld (

IFS

lin

e 1L

.D)

to I

mp

ort

(IF

S l

ine

71)

Fo

reig

n r

eser

ves

in

mo

nth

s o

f im

po

rts

12 m

ch

ang

e R

atio

of

Fo

reig

n r

eser

ves

min

us

go

ld (

IFS

lin

e 1L

.D)

to I

mp

ort

(IF

S l

ine

71)

Fo

reig

n r

eser

ves

12

m %

ch

ang

e IF

S l

ine

1L.D

M2

to r

eser

ves

Rat

io o

f N

arro

w m

on

ey (

IFS

lin

e 34

)+Q

uas

i m

on

ey (

IFS

lin

e 35

) to

Fo

reig

n r

eser

ves

min

us

go

ld (

IFS

lin

e 1L

.D)*

Mar

ket

exc

han

ge

rate

(IF

S

lin

e A

E)/

1000

M2

to r

eser

ves

12

m c

han

ge

Rat

io o

f N

arro

w m

on

ey (

IFS

lin

e 34

)+Q

uas

i m

on

ey (

IFS

lin

e 35

) to

Fo

reig

n r

eser

ves

min

us

go

ld (

IFS

lin

e 1L

.D)*

Mar

ket

exc

han

ge

rate

(IF

S

lin

e A

E)/

1000

Sh

ort

-ter

m c

apit

al f

low

s to

GD

P

R

atio

of

Sh

ort

-ter

m c

apit

al f

low

s (B

IS T

able

8)/

1000

to

GD

P a

t cu

rren

t p

rice

s (I

FS

lin

e 99

B)/

Mar

ket

exc

han

ge

rate

(IF

S l

ine

RF

)

Sh

ort

-ter

m c

apit

al f

low

s to

GD

P

12 m

ch

ang

e R

atio

of

Sh

ort

-ter

m c

apit

al f

low

s (B

IS T

able

8)/

1000

to

GD

P a

t cu

rren

t p

rice

s (I

FS

lin

e 99

B)/

Mar

ket

exc

han

ge

rate

(IF

S l

ine

RF

)

Sh

ort

-ter

m e

xte

rnal

deb

t to

res

erv

es

R

atio

of

Sh

ort

-ter

m e

xter

nal

deb

t (B

IS T

able

9A

B)

to F

ore

ign

res

erv

es m

inu

s g

old

(IF

S l

ine

1L.D

)

Sh

ort

-ter

m e

xte

rnal

deb

t to

res

erv

es

12 m

ch

ang

e R

atio

of

Sh

ort

-ter

m e

xter

nal

deb

t (B

IS T

able

9A

B)

to F

ore

ign

res

erv

es m

inu

s g

old

(IF

S l

ine

1L.D

)

83

TA

BL

E A

4.1

The

Lis

t of L

eadi

ng In

dica

tors

(Con

tinu

ed)

Type

Lea

din

g I

nd

icat

ors

T

ran

sfo

rmat

ion

So

urc

es

Current Account C

urr

ent

acco

un

t b

alan

ce t

o G

DP

Rat

io o

f C

urr

ent

acco

un

t b

alan

ce (

IFS

lin

e 78

AL

)*M

ark

et e

xch

ang

e ra

te (

IFS

lin

e A

F)

to G

DP

at

curr

ent

pri

ces/

1000

Cu

rren

t ac

cou

nt

bal

ance

to

GD

P

12 m

ch

ang

e R

atio

of

Cu

rren

t ac

cou

nt

bal

ance

(IF

S l

ine

78A

L)*

Mar

ket

ex

chan

ge

rate

(IF

S l

ine

AF

) to

GD

P a

t cu

rren

t p

rice

s/10

00

Ex

po

rts

12 m

% c

han

ge

IFS

lin

e 70

Imp

ort

s 12

m %

ch

ang

e IF

S l

ine

71

Rea

l ef

fect

ive

exch

ang

e ra

te,

dev

iati

on

fro

m

tren

d-H

P F

ilte

r B

loo

mb

erg

(JP

Mo

rgan

RE

ER

)

Rea

l ex

chan

ge

rate

ag

ain

st U

S$

dev

iati

on

fro

m

tren

d-H

P f

ilte

r D

om

esti

c C

PI

(IF

S l

ine

64)/

Mar

ket

exc

han

ge

rate

(IF

S li

ne

AE

)*U

S w

ho

lesa

le p

rice

in

dex

(IF

S l

ine

63)*

100/

aver

age(

Do

mes

tic

CP

I(IF

S

lin

e 64

)/M

ark

et e

xch

ang

e ra

te (

IFS

lin

e A

E)

*US

wh

ole

sale

pri

ce i

nd

ex (

IFS

lin

e 63

)

Tra

de

bal

ance

to

GD

P

R

atio

of

Ex

po

rt (

IFS

lin

e 70

)-Im

po

rt (

IFS

lin

e 71

)*M

ark

et e

xch

ang

e ra

te (

IFS

lin

e A

E)

to G

DP

at

curr

ent

pri

ces

(IF

S l

ine

99B

)*10

00

Tra

de

bal

ance

to

GD

P

12 m

ch

ang

e R

atio

of

Ex

po

rt (

IFS

lin

e 70

)-Im

po

rt (

IFS

lin

e 71

)*M

ark

et e

xch

ang

e ra

te (

IFS

lin

e A

E)

to G

DP

at

curr

ent

pri

ces

(IF

S l

ine

99B

)*10

00

Financial Sector D

epo

sits

to

M2

R

atio

of

Dem

and

dep

osi

ts (

IFS

lin

e 24

)+T

ime,

sav

ing

& f

ore

ign

cu

rren

cy d

epo

sits

(IF

S l

ine

25)

to N

arro

w m

on

ey (

IFS

lin

e 34

)+Q

uas

i m

on

ey (

IFS

lin

e 35

)

Dep

osi

ts t

o M

2 12

m c

han

ge

Rat

io o

f D

eman

d d

epo

sits

(IF

S l

ine

24)+

Tim

e, s

avin

g &

fo

reig

n c

urr

ency

dep

osi

ts (

IFS

lin

e 25

) to

Nar

row

mo

ney

(IF

S l

ine

34)+

Qu

asi

mo

ney

(IF

S l

ine

35)

Do

mes

tic

cred

it t

o G

DP

Rat

io o

f D

om

esti

c cr

edit

(IF

S l

ine

32)

to G

DP

at

curr

ent

pri

ces

(IF

S li

ne

99B

)

Do

mes

tic

cred

it t

o G

DP

12

m c

han

ge

Rat

io o

f D

om

esti

c cr

edit

(IF

S l

ine

32)

to G

DP

at

curr

ent

pri

ces

(IF

S li

ne

99B

)

Do

mes

tic

real

in

tere

st r

ate

L

end

ing

rat

e (I

FS

lin

e 60

P)-

Do

mes

tic

CP

I (I

FS

lin

e 64

)

Do

mes

tic

real

in

tere

st r

ate

12 m

ch

ang

e L

end

ing

rat

e (I

FS

lin

e 60

P)-

Do

mes

tic

CP

I (I

FS

lin

e 64

)

Len

din

g-d

epo

sit

rate

sp

read

Len

din

g r

ate

(IF

S l

ine

60P

)-D

epo

sit

rate

(IF

S l

ine

60L

)

Len

din

g-d

epo

sit

rate

sp

read

12

m c

han

ge

Len

din

g r

ate

(IF

S l

ine

60P

)-D

epo

sit

rate

(IF

S l

ine

60L

)

Lo

ans

to d

epo

sits

Rat

io o

f L

oan

s, a

sset

s in

ban

k's

bal

ance

sh

eets

(IF

S 22

A t

o 2

2G)

to D

eman

d d

epo

sits

(IF

S li

ne

24)+

Tim

e, s

avin

gs

& f

ore

ign

cu

rren

cy

dep

osi

ts (

IFS

lin

e 25

)

Lo

ans

to d

epo

sits

12

m c

han

ge

Rat

io o

f L

oan

s, a

sset

s in

ban

k's

bal

ance

sh

eets

(IF

S 22

A t

o 2

2G)

to D

eman

d d

epo

sits

(IF

S li

ne

24)+

Tim

e, s

avin

gs

& f

ore

ign

cu

rren

cy

dep

osi

ts (

IFS

lin

e 25

)

M1

to G

DP

Rat

io o

f N

arro

w m

on

ey (

IFS

lin

e 34

) to

GD

P a

t cu

rren

t p

rice

s (I

FS

99B

)

M1

to G

DP

12

m c

han

ge

Rat

io o

f N

arro

w m

on

ey (

IFS

lin

e 34

) to

GD

P a

t cu

rren

t p

rice

s (I

FS

99B

)

M2

mu

ltip

lier

Rat

io o

f N

arro

w m

on

ey (

IFS

lin

e 34

)+Q

uas

i m

on

ey (

IFS

lin

e 35

)/R

eser

ve

mo

ney

(IF

S l

ine

14)

M2

mu

ltip

lier

12

m %

ch

ang

e R

atio

of

Nar

row

mo

ney

(IF

S l

ine

34)+

Qu

asi

mo

ney

(IF

S li

ne

35)/

Res

erv

e m

on

ey (

IFS

lin

e 14

)

Rea

l co

mm

erci

al b

ank

dep

osi

ts

12 m

% c

han

ge

Dem

and

dep

osi

t (I

FS

lin

e 24

)+T

ime,

sav

ing

s &

fo

reig

n c

urr

ency

dep

osi

ts (

IFS

lin

e 25

)/D

om

esti

c C

PI

(IF

S l

ine

64)

84

TA

BL

E A

4.1

The

Lis

t of L

eadi

ng In

dica

tors

(Con

tinu

ed)

Type

Lea

din

g I

nd

icat

ors

Tra

nsf

orm

atio

n

So

urc

es

Fiscal Account C

entr

al b

ank

cre

dit

to

th

e p

ub

lic

sect

or

to G

DP

Rat

io o

f C

entr

al b

ank

th

e p

ub

lic

sect

or

(IF

S 1

2A, 1

2B, 1

2C &

12B

X)

to G

DP

at

curr

ent

pri

ces

(IF

S li

ne

99B

)

Cen

tral

ban

k c

red

it t

o t

he

pu

bli

c se

cto

r to

GD

P

12 m

ch

ang

e R

atio

of

Cen

tral

ban

k t

he

pu

bli

c se

cto

r (I

FS

12A

, 12B

, 12C

& 1

2BX

) to

GD

P a

t cu

rren

t p

rice

s (I

FS

lin

e 99

B)

Fis

cal

bal

ance

to

GD

P

R

atio

of

Go

ver

nm

ent

fisc

al b

alan

ce (

IFS

lin

e 80

) to

GD

P a

t cu

rren

t p

rice

s (I

FS

lin

e 99

B)

Fis

cal

bal

ance

to

GD

P

12 m

ch

ang

e R

atio

of

Go

ver

nm

ent

fisc

al b

alan

ce (

IFS

lin

e 80

) to

GD

P a

t cu

rren

t p

rice

s (I

FS

lin

e 99

B)

Go

ver

nm

ent

con

sum

pti

on

to

GD

P

R

atio

of

Go

ver

nm

ent

con

sum

pti

on

(IF

S l

ine

91F

) to

GD

P a

t cu

rren

t p

rice

s (I

FS

lin

e 99

B)

Go

ver

nm

ent

con

sum

pti

on

to

GD

P

12 m

ch

ang

e R

atio

of

Go

ver

nm

ent

con

sum

pti

on

(IF

S l

ine

91F

) to

GD

P a

t cu

rren

t p

rice

s (I

FS

lin

e 99

B)

Net

cre

dit

to

go

ver

nm

ent

to G

DP

Rat

io o

f N

et c

laim

s o

n g

ov

ern

men

t (I

FS

lin

e 32

AN

) to

GD

P a

t cu

rren

t p

rice

s (I

FS

lin

e 99

B)

Net

cre

dit

to

go

ver

nm

ent

to G

DP

12

m c

han

ge

Rat

io o

f N

et c

laim

s o

n g

ov

ern

men

t (I

FS

lin

e 32

AN

) to

GD

P a

t cu

rren

t p

rice

s (I

FS

lin

e 99

B)

Global Economy O

il p

rice

IFS

lin

e 76

AA

Oil

pri

ce

12 m

% c

han

ge

IFS

lin

e 76

AA

Rea

l U

S$/

yen

ex

chan

ge

rate

d

evia

tio

n f

rom

tr

end

-HP

fil

ter

100*

Jap

an C

PI

(IF

S li

ne

64)/

(Jap

an m

ark

et e

xch

ang

e ra

te (

IFS

lin

e R

F)*

US

wh

osa

les

pri

ces

ind

ex (

IFS

lin

e 63

))/

aver

age(

Jap

an C

PI

(IF

S

lin

e 64

)/Ja

pan

mar

ket

ex

chan

ge

rate

(IF

S l

ine

RF

)*U

S w

ho

sale

s p

rice

s in

dex

(IF

S li

ne

63)

US

an

nu

al g

row

th r

ate

U

S B

ure

au o

f E

con

om

ic A

nal

ysi

s

US

rea

l in

tere

st r

ate

U

S b

ank

pri

me

loan

rat

e (I

FS

lin

e 60

P)-

Ch

ang

es i

n U

sco

nsu

mer

pri

ces

(IF

S l

ine

64X

)

US

rea

l in

tere

st r

ate

12 m

ch

ang

e U

S b

ank

pri

me

loan

rat

e (I

FS

lin

e 60

P)-

Ch

ang

es i

n U

sco

nsu

mer

pri

ces

(IF

S l

ine

64X

)

Real Sector D

om

esti

c co

nsu

mer

pri

ce i

nd

ex

12 m

% c

han

ge

IFS

lin

e 64

Ind

ust

rial

/m

anu

fact

uri

ng

pro

du

ctio

n

ind

ex

12 m

% c

han

ge

CE

IC /

In

do

nes

ian

Cen

tral

Bo

ard

of

Sta

tist

ics

Sto

ck p

rice

in

dex

in

lo

cal

curr

ency

12

m %

ch

ang

e B

loo

mb

erg

85

TABLE A4.2 The List of Leading Indicators for the Capital Account

Rank Leading Indicators A B C D Good Bad NSR CPC

1 Short-term capital flows to GDP 10 1 66 222 0.1316 0.0045 0.0341 0.9091

2 Short-term capital flows to GDP3 3 1 73 210 0.0395 0.0047 0.1201 0.7500

3 Foreign reserves in months of imports 2 2 74 222 0.0263 0.0089 0.3393 0.5000

4 Foreign reserves2 12 12 64 211 0.1579 0.0538 0.3408 0.5000

5 Foreign reserves in months of imports3 20 20 56 192 0.2632 0.0943 0.3585 0.5000

6 Domestic real interest rate differential from US rate3 3 29 1 73 0.7500 0.2843 0.3791 0.0938

7 Deposits in BIS banks to reserves3 4 24 0 30 1.0000 0.4444 0.4444 0.1429

8 M2 to reserves3 10 17 66 206 0.1316 0.0762 0.5794 0.3704

9 M2 to reserves 1 2 75 233 0.0132 0.0085 0.6468 0.3333

10 Domestic real interest rate differential from US rate 3 24 8 83 0.2727 0.2243 0.8224 0.1111

11 Short-term external debt to reserves3 40 117 36 106 0.5263 0.5247 0.9969 0.2548

12 Foreign liabilities to foreign assets3 34 130 42 93 0.4474 0.5830 1.3031 0.2073

13 Short-term external debt to reserves 4 21 72 214 0.0526 0.0894 1.6979 0.1600

14 Foreign liabilities to foreign assets 4 49 72 186 0.0526 0.2085 3.9617 0.0755

15 Deposits in BIS banks to reserves 0 6 4 60 0.0000 0.0909 #DIV/0! 0.0000

TABLE A4.3 The List of Leading Indicators for the Current Account


1 Current account balance to GDP 11 1 41 126 0.2115 0.0079 0.0372 0.9167

2 Real effective exchange rate1 45 33 31 202 0.5921 0.1404 0.2372 0.5769

3 Exports2 22 19 54 204 0.2895 0.0852 0.2943 0.5366

4 Trade balance to GDP3 5 5 71 218 0.0658 0.0224 0.3408 0.5000

5 Current account balance to GDP3 7 8 36 116 0.1628 0.0645 0.3963 0.4667

6 Real exchange rate against US$1 23 37 53 198 0.3026 0.1574 0.5203 0.3833

7 Trade balance to GDP 27 83 49 152 0.3553 0.3532 0.9942 0.2455

8 Imports2 10 54 66 169 0.1316 0.2422 1.8404 0.1563

TABLE A4.4 The List of Leading Indicators for the Financial Sector


1 Loans to deposits3 12 8 64 215 0.1579 0.0359 0.2272 0.6000

2 M1 to GDP3 9 6 67 217 0.1184 0.0269 0.2272 0.6000

3 Domestic real interest rate3 4 27 0 75 1.0000 0.2647 0.2647 0.1290

4 M1 to GDP 5 5 71 230 0.0658 0.0213 0.3234 0.5000

5 Lending-deposit rate spread 6 23 5 84 0.5455 0.2150 0.3941 0.2069

6 Deposits to M23 6 7 70 216 0.0789 0.0314 0.3976 0.4615

7 Domestic real interest rate 4 22 7 85 0.3636 0.2056 0.5654 0.1538

8 M2 multiplier2 50 91 26 132 0.6579 0.4081 0.6203 0.3546

9 M2 multiplier 37 71 39 164 0.4868 0.3021 0.6206 0.3426

10 Domestic credit to GDP3 30 64 46 159 0.3947 0.2870 0.7271 0.3191

11 Real commercial bank deposits2 58 161 18 62 0.7632 0.7220 0.9460 0.2648

12 Domestic credit to GDP 44 184 32 51 0.5789 0.7830 1.3524 0.1930

13 Loans to deposits 4 76 72 159 0.0526 0.3234 6.1447 0.0500

14 Deposits to M2 0 3 76 232 0.0000 0.0128 NA 0.0000

15 Lending-deposit rate spread3 0 43 4 59 0.0000 0.4216 NA 0.0000

86

TABLE A4.5 The List of Leading Indicators for the Fiscal Account


1 Government consumption to GDP 65 67 11 168 0.8553 0.2851 0.3334 0.4924

2 Government consumption to GDP3 48 53 28 170 0.6316 0.2377 0.3763 0.4752

3 Net credit to government to GDP3 14 14 38 113 0.2692 0.1102 0.4094 0.5000

4 Fiscal balance to GDP3 32 40 44 183 0.4211 0.1794 0.4260 0.4444

5 Fiscal balance to GDP 21 39 55 196 0.2763 0.1660 0.6006 0.3500

6 Central bank credit to the public sector to GDP 24 55 52 180 0.3158 0.2340 0.7411 0.3038

7 Net credit to government to GDP 26 114 26 25 0.5000 0.8201 1.6403 0.1857

8 Central bank credit to the public sector to GDP3 10 49 66 174 0.1316 0.2197 1.6700 0.1695

TABLE A4.6 The List of Leading Indicators for the Global Economy


1 Real US$/yen exchange rate1 11 1 65 234 0.1447 0.0043 0.0294 0.9167

2 US annual growth rate 11 2 65 233 0.1447 0.0085 0.0588 0.8462

3 US real interest rate3 4 1 72 222 0.0526 0.0045 0.0852 0.8000

4 US real interest rate 17 7 59 228 0.2237 0.0298 0.1332 0.7083

5 Oil price 63 121 13 114 0.8289 0.5149 0.6211 0.3424

6 Oil price2 0 6 76 217 0.0000 0.0269 NA 0.0000

TABLE A4.7 The List of Leading Indicators for the Real Sector


1 Stock price index in local currency2 7 55 21 58 0.2500 0.4867 1.9469 0.1129

2 Industrial/manufacturing production index2 0 0 4 79 0.0000 0.0000 NA NA

3 Domestic consumer price index2 0 27 76 196 0.0000 0.1211 NA 0.0000

87

CHAPTER 5

PREDICTING INDONESIA CURRENCY CRISES

USING THE DISCRETE CHOICE MODEL

5.1. Introduction

This chapter is the second chapter dedicated to development of an early

warning system (EWS) model to predict Indonesia’s currency crisis episodes.

Towards this goal, this chapter will employ the discrete choice probit and logit

models to predict these crisis episodes. Unlike the signal approach that was

applied in previous chapter, the logit/probit models simultaneously evaluate

the overall effects of the explanatory variables (Komulainen and Lukkarila,

2003). In addition, information that includes the probability of a crisis

occurring, and marginal contribution of each indicator, can easily be

summarized. It is also possible to apply a standard statistical test to assess the

model (Komulainen and Lukkarila, 2003). This study differs from previous

studies that have applied the probit/logit model in explaining and predicting

the currency crises and which have mostly involved multiple country analysis

by focusing on a single country. In addition, to capture the recent crisis

episodes, this study extends the sample period up to September 2008.

The discussion is organized as follows. Section 5.2 describes the previous

literature on the application of a probit/logit EWS model. Section 5.3 discusses

the basic concepts of the proposed EWS model. Section 5.4 presents the

empirical findings of the application of probit model in predicting the

Indonesian currency crises. Finally, the conclusion of this chapter will be

presented in Section 5.5.

88

5.2. Literature Review

This section explores and discusses the previous studies that applied the

discrete choice logit and probit models. Studies in this field can be divided into

two groups. Some studies attempt to explain the phenomena of currency crises

and to highlight the common factors related to these events. Other studies focus

on applying the discrete choice model as an EWS model to predict future

currency crises.

In examining the common determinant factors of the crises, most studies have

used multi-country or cross-country analyses in two ways. The first approach

points out that the currency turmoil was mainly caused by the deteriorating

domestic economic and financial circumstances. The second approach argues

that in the 1997 Asian Financial Crisis, a sudden shift in market expectations

and confidence led to regional and global contagion.

Examining the situation, Frankel and Rose (1996) applied a probit model to

analyze currency crashes for 105 developing countries from 1971 to 1992. They

found that low foreign direct investment (FDI), low reserves, high domestic

credit growth, high northern interest rates, and overvaluation of the real

exchange rate tended to trigger the occurrence of a currency crash.

Furthermore, they noted that current account and government budget deficits

could not explain the currency crises. Similarly, Goldfjan and Valdés (1997a),

who analyzed the situation three-months ahead of the binary crisis using a

panel of data from 26 countries, from May 1984 to May 1997, found that the real

exchange rate overvaluation had predictive power in explaining crises, but that

market expectations failed when it came to predicting currency crises.

Falcetti and Tudela (2006) argued that a currency crisis is a dynamic event with

each past crisis having an effect on later crisis events. Employing a dynamic

probit model by adding a lagged dependent variable among their regressors in

92 developing and emerging markets from 1970 to 1997, they found that

macroeconomic variables, financial debt and global variables, as well as the

previous banking crisis could determine the presence of the currency crisis. The

89

underlying factors of each currency crisis differed by types of exchange rate

regime, and moreover, countries that sharply devalued in the past were less

prone to experience a currency crisis in the future.

Komulainen and Lukkarila (2003) indicated that the currency crisis could be

explained by looking at indicators that included US interest rates, indebtedness

variables, such as private sector liabilities, public debt, and foreign liabilities of

banks, as well as traditional variables, such as unemployment and inflation, and

banking crises. Edwards (1989) also noted that the probability of devaluation

increased with the appreciation of the real exchange rate and the deterioration

of foreign asset position of the central bank. Isolating variables that are the

important predictors of a crisis, Berg and Pattillo (1999b) found that the

probability of a currency crisis increased when the bilateral real exchange rate

was overvalued relative to its trend, or when there was low reserve growth, low

export growth, and high growth of M2/reserves. In addition, they pointed out

that the presence of a large current account deficit and high ratio of M2 to

reserves increased the risk factor. In another study, Berg and Pattillo (1999a)

pointed out that high credit growth, an overvalued real exchange rate relative

to its trend, and a high ratio of M2 to reserves, as well as a large current account

deficit, increased the probability of a crisis.

Unlike other studies, Lau and Isabel (2005) argued that the standard probit

model cannot capture the role of a central bank in defending their domestic

currency against speculation because the prediction is only between occurrence

(1) and non-occurrence (0) in relation to a speculative attack on the currency. By

using a multi-state nested logit model, they could measure the probabilities of

speculative attacks and the probabilities of successful defenses by using nine

quarterly explanatory variables for 16 countries. They found that the

probability of speculative attacks increased whenever there was a high ratio of

short-term external liabilities to foreign reserves, a large fiscal deficit, and

appreciation of the real exchange rate.

In contrast, in the case of Korea, Park and Rhee (1998) argued that

macroeconomic indicators failed to predict the crisis because the pre-crisis

90

trends for most explanatory variables were still positive. They highlighted that

financial liberalization tends to expose the possibility of speculative attacks on

domestic financial markets. Furthermore, their study shows that a currency

crisis can hit any country and that even their domestic financial markets are

partially and indirectly open to foreign investors. Komulainen and Lukkarila

(2003) found that the capital account liberalization in the intermediate exchange

rate regimes increased the occurrence probability of the currency crises.

Examining the timing of a crisis, they found that the currency crises tended to

occur two-years after the liberalization of domestic financial sectors, or 4.5 years

after the liberalization of capital flows. Moreover, in determining the common

factors of the speculative attack on currencies, their model considers a set of

domestic political and economic variables. Eichengreen et al. (1996), also find a

contagion1 variable as the determining factor, which is statistically significant.

In this part of the study the application of the discrete choice probit/logit as an

EWS model to predict the currency crisis will be discussed. In relation to the

previous empirical work about crisis models, potential explanatory variables

are provided that may be useful for constructing and improving the

performance of an early warning system model. Even though Goldfjan and

Valdes (1997a) argued that “exchange rate crises are largely unpredictable

events”, some researchers prove that the logit/probit early warning system

model is a useful tool to predict crises. As examples, Esquivel and Larraín

(1998) were able to predict correctly most of the currency crises that occurred

within their sample, and support an EWS as a tool to prevent such currency

crises. Jacobs et al. (2005) also showed that their EWS model could perform well

in predicting the currency crises in six Asian countries they studied. Their

model indicates that out-of-sample forecasts are significantly better than in-

sample projections.

Unlike the other researchers, Berg and Pattillo (1999a) apply and compare three

different EWS models to predict the Asian Financial Crisis: namely the panel-

1 Eichengreen et al. (1996), defined a contagion variable as the presence of either a successful or

unsuccessful speculative attack on currency elsewhere in the world.

91

based signals approach based on Kaminsky et al. (1998); the probit model based

on Frankel and Rose (1996); in addition to the cross-country regressions model

based on Sachs et al. (1996). They found that only one model, that is the signal

approach, was able to predict the crisis, but that its predictive results were not

reliable. In another work, Berg and Pattillo (1999b) compared the performance

of the signal approach and probit model approach. They showed that their

models were also able to predict the occurrence of future crises. Examining their

conclusions, which tend to be unambiguous because the signal forecasting

approach performs better than some probit models, it would appear that the

probit model is better in terms of score and goodness-of-fit, additionally; the

linear specification performs better when compared to other probit models.

In conclusion, the previous studies focusing on determining the underlying

factors of currency crises and developing sophisticated models to predict crises

are still being perfected. Nonetheless, despite the studies under review, no

specific analysis or case study has been developed for Indonesia as a single

country analysis. Hence, it is necessary to develop a specific model that is viable

for that country. The evidence of various studies in this field are somehow

mixed and not robust. However, these studies suggest that the forecasting

models may help to indicate vulnerability of crises even if their current

predictive powers are still limited.

This study sets out to predict currency crises and to discover the underlying

factors. However, in dealing with the mixed results and limited prediction

power of previous works that mostly use cross-country analysis, this study

utilizes a probit/logit EWS model and applies a single country analysis for

Indonesia. By doing so, it might be able to capture the uniqueness and the

characteristic of this country that may in turn improve the prediction power of

the models. This study will also differ from previous studies related to the way

explanatory variables are selected in that this study applies the noise-to-signal

ratio that is usually used on the non-parametric signal model. The study also

extends the sample period until 2008 to see the capabilities of this model in

capturing any other crises that may exist during this period. The

92

comprehensive explanations of the model will be presented in the following

sections.

5.3. The Discrete Choice Probit/Logit Model

Using the limited dependent variable that has value between “1” for “crisis”

and “0” for “no crisis” is not appropriate when used with the standard OLS

model. This is because there is no guarantee that �� i, the estimator of �� can be restricted to a range between 0 to 1 (Maddala, 1983, Gujarati, 2004).

Therefore, this study follows the method of Eichengreen et al. (1995), and

Frankel and Rose (1996) which apply a probit/logit model in estimating the

binary currency crises variables. The difference between them is that while the

probit is based on the cumulative standard normal distribution that allows the

estimated values of the dependent variable in a range of [0;1], the logit uses the

cumulative logistic distribution to constrain the probability to the [0,1] interval

(Jacobs et al., 2005). Reference to this can be seen in econometric text books

such as Maddala (1983), Greene (1996), and Gujarati (2004), that provide more

detailed discussion on this model.

Empirically, it is assumed that there is an unobserved variable, or a latent

variable (�∗), which is described by the equation:

�∗ = �� + ��5.1� where β is the regression coefficient of the independent variable, xi is a set of

explanatory variables, and εi is the error term. The outcome of a discrete choice

model is a reflection of the regression in Equation 5.1. In other words, the

observed data (yi) is determined by the value of unobservable or latent variable

(�∗). More specifically, the positive value for �∗ will indicate the presence of a

crisis in Indonesia (yi=1), while zero or negative value for �∗ reflects no crisis events (yi=0) or

93

� = �1��∗ > 0��0��ℎ�� ! ��5.2� If the distribution of εi is symmetric, then

� = #10! ��with�(��)�� > −�� = +�� with�(��)�� ≤ −�� = 1 − +�� 5.3�

where F is the cumulative distribution function for εi. This limited dependent

variable model is then estimated, based on the maximum likelihood approach,

for which the likelihood function is:

. = /01 − +�� 1234

/+�� 235

��5.4�

= /01 − +�� 157280+�� 1289

�35��5.5�

where yi=(0,1) is the realization of the binary outcome.

Taking logs,

:;�. = <=�1 − ��:;01 − +�� 1 + �:;+�� >9

�35��5.6�

The first order condition is:

@:;�.@� = <A�� +�� + −�1 − �� 1 − +�� B �� = 0

9

�35��5.7�

where f is the density, DEFG8HIJDG8HI .�This likelihood equation will be nonlinear,

requiring an iteration solution. The type of binary model is determined by the

choice of the functional form for F in Equation 5.4, connected to the

assumptions made about εi in Equation 5.1.

The Probit Model

If the error term follows the standard normal distribution, ε~N(0,1), the model

is referred to as a probit, expressed as:

94

��Prob�� = 1� = F�� = P QG8HI7R

��S��5.8� = Φ��

where Q�� is the standard normal density. By inserting Equation 5.8 into

Equation 5.7, the likelihood equation is:

:;�. = <V�1 − ��:;F1 − Φ�� J + �:;FΦ�� JW9

�35��5.9�

taking the first order condition,

@:;�.@� = <Y�� = <Z[�Q�[�� Φ�[�� \�� = 0

9

�35

9

�35��5.10�

where qi=2yi-1

5.4. The Application of the Probit/Logit EWS Model to Predicting


5.4.1. Constructing the Probit/Logit EWS Model

In predicting the currency crises in which the dependent variable is defined as a

dichotomous variable that takes the value of unity for currency crisis

occurrence and zero for no crisis, this study applies the discrete choice probit

and logit models using Equations 5.8 and 5.11. However, in the empirical

analysis the predicted probabilities calculated by both models differ only

slightly2, thus, this study only presents the analysis based on the probit model.

As in the previous chapter, when defining the currency crisis event3, this study

follows the study of Kaminsky et al. (1998) which defines the currency crisis as

2 Jacobs et. al (2005), Komulainen and Lukkarila (2003), Maddala (1993). 3 See Chapter 3 for a more detailed discussion about the currency crisis dating system.

95

an attack on the domestic currency, leading to a large depreciated domestic

currency against the US dollar, or a large depletion in the foreign reserve, or the

combination of both conditions. Furthermore, in the empirical work, the

combination of these variables can be transformed into the exchange market

pressure index (EMPI) and the currency crisis defined whenever the EMPI

crosses its threshold, three standard deviations above the index’s mean. Based

on this definition, the EMPI crossed its threshold eight times, namely

November 1978, April 1983, September 1986, August 1997, December 1997,

January 1998, May 1998 and June 1998. Following Kaminsky et al. (1998), this

study applied a 24 month crisis window or prediction horizon, so resulting in

only four currency crises episodes occurring in Indonesia from 1970 to 2008.

Three of the crises occurred in the in-sample period, and one crisis, the 1997/98

Asian Financial Crisis, happened in the out of sample period from 1996 to 2008.

Moreover in selecting the set of explanatory variables, based on the EWS and

currency crises literatures, the numbers of variables that are available and

potentially used to determine currency crises is extensive. For example, in the

previous chapter that used a non-parametric approach to predict the

Indonesian currency crises, the signal approach selected 39 of 55 explanatory

variables based on their noise-to-signal ratios being less than unity (NSR<1).

However, according to Jacob et al. (2005), and Zhuang and Dowling (2002), the

probit/logit model cannot accommodate all explanatory variables because of

too few observations and multicollinearity among its explanatory variables. In

addition, according to Kamin et al. (2007), using a large set of explanatory

variables in the probit/logit model, potentially increased the number of

explanatory variables that were statistically insignificant, in addition to

introducing a certain noise in the estimation results.

Following their argument, to limit the number of explanatory variables (unlike

Jacob et al. (2005), who used factor analysis), this study uses the noise-to-signal

ratio (NSR) from Kaminsky et al. (1998), which is commonly used in the signal

approach for evaluating and selecting variables. This method was used in the

previous chapter for evaluating and selecting the leading indicators that were

96

used to construct the composite index in the signal approach. The ranked

results are based on the lowest NSR, which utilises the most powerful indicator

in predicting crises and which was presented in Table 4.2 in Chapter 4.

Following this procedure, this study selects the top ten variables, which are

presented in Table 5.1.

9

7

TABLE 5.1 L

ist

of E

xpla

nat

ory

Var

iabl

es

No

Leading Indicators

NSR

Rank

Reference

Rationale

01

Real US$/yen exchange rate

a 0.03

01

Zhuang and Dowling (2002), Berg (1999), Ito (1999)

According to Zhuang and Dowling (2002), the depreciation of the Japanese yen

against the US dollar could pressure the regional East Asian currencies. In

addition, the fluctuation of the yen/dollar rate around PPP m

ake the business

cycle in the East Asian countries more volatile (McK

innon and Schnabl, 2003).

02

Short-term capital flows to

GDP

0.03

02

Rad

elet and Sachs (1998), Komulainen and Lukkarila

(2003)

The capital outflows are highly correlated with the currency crises in m

ost crises

events (Goldfajn and Valdes, 1997b). In m

any cases, the capital inflows increase

prior to crises and there is the presence of huge capital flight during the crises. In

addition, a sudden capital outflow m

ay cause bank failure (Calvo et al., 1993)

03

US annual growth rate

0.06

04

Kam

in et al. (2007), Zhuang and Dowling (2002)

For growth of the World economy the US economy can be associated with world

dem

and. Decline in U

S growth rate can reduce dem

and for im

ports. D

eclining

export will cause deterioration of the current account am

ong US trading partners

that increases the probability of a crisis.

04

US real interest ratec

0.09

05

Kam


Increases in w

orld interest rates, especially U

S real interest rates, lead

s to an

increase in the likelihood of a currency crisis by attracting the capital outflows

from developing countries, such as Indonesia.

05

US real interest rate

0.13

07

Kam


06

Loans to deposits

c 0.23

08

Zhuang and D

owling (2002), B

ussiere and Fratzscher

(2002), Jacob et al. (2005)

There is a closed link between banking crisis and currency crisis (Falcetti and

Tudela, 2006, Zhuang and Dowling, 2002). M

any variables can be used to view

the perform

ance of the banking sector; one of them

is the loan-deposit rate

spread

. Furthermore, a widening gap between lending-deposit rates can reflect

problems in the banking sector(Zhuang and Dowling, 2002).

07

M1 to GDPc

0.23

09

Bussiere and Fratzscher (2002), Tinakorn (2002)

M1 can be used to determine the liquidity in an economy. Thus, increasing M

1 will increase the liquidity, which m

ay lead to a speculative attack on domestic

currency (Eichengreen et al., 1995)

08

Real effective exchange rate

a 0.24

10

Kam

in et al. (2007), Berg (1999), C

hinn (1998), Frenkel

and G

oldstein (1989), Tornell (1998), Berg and Pattillo

(1999b), Esquivel and Larrain B (1998), Kumar et al

(2003),

The overvaluation of domestic currency reduces the country’s competitiveness

that potentially reduces their exports. This condition affects a deterioration in the

current account that leads to a currency crisis in m

any countries, as well as the

banking crisis through corporate distress that increases the non-perform

ing loan

(Zhuang and Dowling, 2002).

09

Exportsb

0.29

12

Berg and Pattillo (1999b), Komulainen and Lukkarila

(2003), Kumar et al (2003), Zhuang and Dowling (2002),

Bussiere and Fratzscher (2002),

Tinakorn (2002),

Kam

insky et al. (1998), Kam

in et al. (2007), Berg et al.

(2005), Jacob et al. (2005)

10

M1 to GDP

0.32

13

Bussiere and Fratzscher (2002), Tinakorn (2002), Berg et

al. (2005), Jacob et al. (2005)

Note: a deviation from trend-H

P filter, b 12 months percentage change, c 12 months change

98

Based on this table, all variables except for three, start from 1971, namely: the

current account balance to GDP; the 12 month change of short-term capital

flows to GDP; and the 12 month change of the domestic real interest rate.

Unlike the signal approach, and according to Mironova (2007), the probit/logit

model requires consistent data intervals, so variables measured at different

points in time will be eliminated and replaced by other variables which are

ranked below. Table 5.1 lists ten explanatory variables that will be used by the

probit/logit models.

After the discussion of explanatory variables considered in this study, the

descriptive statistics of these variables are presented in following Table 5.2.

TABLE 5.2 The Descriptive Statistics

SYMBOL MAX MIN MEAN MEDIAN STDEV # OF SAMPLE

kx8 0,03 0,00 0,01 0,01 0,01 300

cy1 518,00 -47,15 22,42 12,44 51,13 300

cy4 72,26 -55,86 1,86 -0,09 27,26 300

fx3 0,12 0,06 0,09 0,09 0,01 300

fy3 0,02 -0,02 0,00 0,00 0,01 300

fy7 0,56 -0,73 -0,02 0,01 0,21 300

gx1 10,46 -2,72 3,86 3,85 2,92 300

gy1 12,14 -3,38 0,16 0,16 2,13 300

gy3 7,19 -1,94 3,09 3,38 2,20 300

gy4 39,58 -23,25 0,88 0,83 11,26 300 Note: Real US$/yen exchange ratea (gy4); Short-term capital flows to GDP (kx8), US annual growth rate (gy3), US real interest ratec (gy1); US real interest rate (gx1), Loans to depositsc (fy7), M1 to GDPc (fy3); Real effective exchange ratea (cy4); Exportsb (cy1); M1 to GDP (fx3); a deviation from trend-HP filter, b 12 months percentage change, c 12 months change;STDEV: standard deviation.

Following Jacob et al. (2005), this study checks whether this data set shows

correlation among two or more variables by using the correlation matrix. The

results are presented in Table 5.3. Based on the correlation matrix in Table 5.3,

this study finds that there is no multicollinearity in the data set.

99

TABLE 5.3 The Correlation Matrix

gy4 kx8 gy3 gy1 gx1 fy7 fy3 cy4 cy1 fx3

gy4 1.00

kx8 0.07 1.00

gy3 0.14 -0.04 1.00

gy1 0.09 -0.01 0.20 1.00

gx1 -0.27 -0.13 0.13 0.40 1.00

fy7 -0.09 0.07 -0.02 -0.06 0.16 1.00

fy3 -0.02 0.14 -0.18 0.05 -0.12 -0.04 1.00

cy4 -0.31 0.34 -0.06 0.07 0.09 0.04 0.13 1.00

cy1 0.13 0.12 -0.11 -0.11 -0.34 0.11 -0.12 -0.03 1.00

fx3 0.05 -0.07 -0.07 0.07 0.31 0.09 0.25 0.20 -0.28 1.00

Note: Real US$/yen exchange ratea (gy4); Short-term capital flows to GDP (kx8), US annual growth rate (gy3), US real interest ratec (gy1); US real interest rate (gx1), Loans to depositsc (fy7), M1 to GDPc (fy3); Real effective exchange ratea (cy4); Exportsb (cy1); M1 to GDP (fx3); a deviation from trend-HP filter, b 12 months percentage change, c 12 months change

5.4.2. Estimation Results

To forecast the currency crises in Indonesia, this study, using these ten

explanatory variables described above, estimates the probit model with

Huber/White robust errors and covariance, which is robust to misspecification

of the correlation within groups. In this table, the coefficients of variables

cannot be interpreted as the marginal effects of variable to the probability of a

crisis, but only to determine the direction effect of these variables on the

probability of a crisis. In addition, the numbers in parentheses are the z-

statistics as an analogue to the t-statistics in standard OLS regression that test

the null hypothesis of no significance, and an asterisk categorizes the level of

statistical significance of the explanatory variables.

Table 5.4 presents the regression results on the determinants of currency crises

in Indonesia, but the regression results are mixed and not all of them are in

accordance with what would have been expected. For example, almost all of the

variables have signs as predicted, except for the US annual growth rate and the

12 month percentage change of the US real interest rate. However, none of these

variables are statistically significant, whereas of eight variables with correct

signs, three are statistically insignificant, namely the real US$/yen exchange

100

rate when using a deviation from trend-HP filter, the US real interest rate, and

M1 to GDP.

Based on Table 5.4, the estimation results indicate that the significant predictors

for the Indonesian currency crises are (1) short-term capital flows to GDP; (2)

loans to depositsc; (3) M1 to GDPc; (4) real effective exchange ratea; and (5)

exportsb. The regression results in the following table also support Kamin et al.

(2007), who show that the use of many variables often produces a number of

variables that are not statistically significant. Furthermore, the use of monthly

data also creates imbalances in the sample because of too many tranquil periods

compared to infrequent times of crisis (Esquivel and Felipe, 1998).

TABLE 5.4 The General Model (Model 1)’s Regression Results

Variables Expected sign Regression coefficient

Constant -3.806) (-2.175)

**

Real US$/yen exchange ratea - -0.002) (-0.175)

Short-term capital flows to GDP

- -91.104) (-3.964)

*

US annual growth rate - 0.097) (1.175)

US real interest ratec + -0.061) (-1.264)

US real interest rate + 0.065) (1.231)

Loans to depositsc + 1.405) (3.238)

*

M1 to GDPc + 31.081) (1.828)

***

Real effective exchange ratea + 0.049) (4.323)

*

Exportsb - -0.017) (-2.962)

*

M1 to GDP + 28.212) (1.495)

McFadden R2 0.597) Number of observation 300.000) Note: a deviation from trend-HP filter, b 12 months percentage change, c 12 months change; * indicates statistical significance at a 1% level, ** indicates significance at a 5% level, and *** indicates significance at a 10% level

101

When determining the contributory factors related to currency crises in

emerging markets, Kamin et al. (2007) initially applied the larger or broad

regression model. They then dropped the least significant explanatory variable

from their “broad model” until all explanatory variables were statistically

significant, leading to their “boiled down model”. In other words, only the

significant variables were retained in the model. This study also found several

insignificant variables in the initial regression model. To make it simple,

sequentially the least significant variables were removed from the “general

model” or “model 1” until all variables were significant. In this study, this new

model is called the “specific model” or “model 2”.

Based on the regression results in Table 5.4, there are five variables that are not

significant, the real US$/yen exchange ratea being the least significant. Once

this variable is dropped, the model is re-estimated. The results indicate that

there are still four insignificant variables, with the US annual growth rate being

the least significant. After this is dropped, the model is regressed again and two

insignificant variables found. Of these two, M1 to GDPc is the least significant

variable. After deleted this variable, the model is re-estimated and only one

insignificant variable is found, namely the US real interest rate. However, after

this is dropped, the model still shows one insignificant variable, namely the US

real interest ratec. Finally after deleting this variable, the model is regressed

again and as expected it is found that all the remaining five variables are

significant and have signs as expected. The regression results are presented in

Table 5.5.

102

TABLE 5.5 The Specific Model (Model 2)’s Regression Results

Variables Expected sign Regression coefficient

Constant -2.895) (-2.300)

**

Short-term capital flows to GDP - -81.811) (-3.753)

*

Loans to depositsc + 1.440) (3.141)

*

Real effective exchange ratea + 0.052) (6.113)

*

Exportsb - -0.019) (-3.358)

*

M1 to GDP + 24.566) (1.797)

***

McFadden R2 0.597) Number of observation 300.000) Note: a deviation from trend-HP filter, b 12 months percentage change, c 12 months change; * indicates statistical significance at a 1% level, ** indicates significance at a 5% level, and *** indicates significant at a 10% level

To determine the relative contribution of the remaining five variables on the

probability of currency crises in Indonesia, this study also calculates the

marginal effect for all variables, as presented in Table 5.6. This table shows that

the most influential factors for the occurrence of currency crises in Indonesia are

the short-term capital flows to GDP, followed by M1 to GDP, loans to deposits,

real effective exchange rate, and exports.

TABLE 5.6 Determinants of Indonesian Currency Crises

No Leading Indicators Expected Sign dprob/dx

01 Short-term capital flows to GDP - -9.600 02 M1 to GDPc + 2.883 03 Loans to depositsc + 0.167 04 Real effective exchange ratea + 0.006 05 Exportsb - -0.002

Note: Note: a deviation from trend-HP filter, b 12 months percentage change, c 12 months change; dprob/dx coefficient represents the marginal effect of the change in explanatory variable to the change of the probability of a crisis.


In this section, the study tests the predictive power of these two models by

simulating the probability of a currency crisis, utilising Figures 5.1 and 5.2. The

predictions will be divided into two periods: the in-sample period ranging from

January 1971 to December 1995, which is presented in Figure 5.1, and the out-

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of-sample period ranging from January 1996 to September 2008, which is

presented in Figure 5.2. The “general model” or model 1 is represented by a

black solid line (or probit_M1), and the “specific model” or model 2 is

represented by the colour red (or probit_M2). In addition, the yellow shaded

areas (or cc_24m) show the 24-month pre-currency crisis episodes. The

performance of these models is reported in Table 5.6 in the next section.

In-sample Prediction

As indicated in Figure 5.1, both models move in the same pattern and perform

well in capturing all three in-sample currency crises, namely November 1978,

April 1983 and September 1986. In predicting the crisis in November 1978, the

model sends warning signals for Indonesia, with the probability being about 76

percent for model 1 and 82% for model 2 in August 1976. This is then reduced

to 63% before shifting to around 80 percent in December 1976. The probability

remains high until January 1978, or 10 months before the crisis occurred, as the

probability of a crisis declined to around 40% for model 1 and 37% for model 2.

Then their probabilities tend to fluctuate with the average being about 50%

until the crisis date in November 1978.

For the next crisis, both models start sending warning signals from April 1983

with the probability about 50% that a crisis would happen in Indonesia within

24 months. Afterwards, the probability increases gradually and remains high at

around 98%. In the last in-sample currency crisis, both models are also able to

predict this crisis by sending warning signals from November 1983 or 36

months before the crisis date with the probability of Indonesia being hit by a

crisis being about 18% for model 1 and 34% for model 2. These probabilities

then increase gradually and reach 97% for model 1 and 95% for model 2. After

that their probabilities decrease gradually to below 10% in September 1986.

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FIGURE 5.1 The Probit Models: In-Sample Prediction

However, both models also send false signals because these signals are not

followed by any currency crises within 24 months. For example, from January

1993, or one month after the Bank of Indonesia closed Bank Summa

International, one of the ten largest private banks in Indonesia, the probability

of a crisis for both models increased gradually from 14% to 82% for model 1 and

75% for model 2 in December 1993, before falling significantly a month later.

Out-of-Sample Prediction

Testing the ability of this model to predict a currency crisis, this study attempts

to predict the out-of-sample currency crises from January 1996 to September

2008. According to the model crisis definition based on the Equation 3.2 in

Chapter 3, the Asian Financial Crisis in 1997/98 was the only one out-of-sample

currency crisis during this period. This is shown in the model of probability of a

crisis displayed in Figure 5.2. In this figure, the probability of a crisis for both

models also moves in the same pattern along the out-of-sample periods.

In predicting the Asian Financial Crisis in 1997/98, both models start sending

warning signals for the crisis in January 1997 with the probability of a crisis

about 66% for model 1 and 50% for model 2. In June 1997, this increases

gradually to 80% for model 1 and 70% for model 2. After that their probability

0%

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tends to decline to around 10% in October 1997 before they increase to reach to

100% in January 1998, where they remain until the crisis date in June 1998.

FIGURE 5.2 The Probit Models: Out-of-Sample Prediction

Although by the model’s crisis definition, there was no longer any currency

crisis occurring after the Asian Financial Crisis of 1997/98 to 2008, both models

still sent out warning signals, as their probability of a crisis tended to increase

during this period. As the purpose of this study is to develop EWS models to

predict currency crises in Indonesia, any signal transmitted by the models but

not followed by any currency crises within 24 months are categorized as false

signals. From Figure 5.2, it is also clear that both models have sent more false

signals in the last decade.

For example the probability of a crisis in both models remained high until

December 1999. In addition, even though not too significant, the probability of a

crisis in the two models rose from July to September 2000. Furthermore, the

probability of a crisis in both models also increased significantly to 100% from

April to June 2001, but had dropped in July 2001, only to rise again in August to

peak above 90% in December 2001. It remained at this high level until March

2002. Similarly, in July 2003, the probability of a crisis for both models also

fluctuated, for after a rise to 80% for 3 months, it then fell, but rose again to

0%

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around 50% from April to June 2004. Furthermore, the probabilities of the two

models also tended to rise towards 2008.

Interestingly those false signals occurred before, or coincided with, serious

domestic political events. The link between political activity and the crises

might explain why the probability of a crisis in both models tended to rise

during these periods. Furthermore, some scholars have found there are strong

links between political events or activities leading to financial crises. For

example, the 1997/98 currency crisis in Indonesia transformed into a multi-

dimensional crisis which affected the banking and financial sector, social and

political spheres, and culminated in a national leadership crisis that finally

ended 32 years of the “New Order” regime in 1998 (Djiwandono, 2001). After

President Soeharto stepped down in May 1998, he was replaced by Vice

President BJ Habibie as the third president of Republic of Indonesia who

remained in that position until the election of a new president in the general

election of 1999. The above figure also shows that the probability of a crisis in

both models during the transition from the “new order” regime to the

“reformation” regime was also high. These results are in accord with the results

of the study of Ghosh and Ghosh (2003), which indicate that during the period

of transition government the country became more vulnerable to

macroeconomic shocks and corporate sector weaknesses.

The link between the crisis and political activity appears to have also applied to

Indonesia. For example, the in-sample currency crisis events in 1978, 1983 and

1986 occurred just before or after general election in 1977, 1982 and 1987.

Furthermore, the Asian Financial Crisis in 1997 happened to coincide with the

general election of 1997. This is consistent with studies conducted by Mei and

Guo (2004) which found the relationship between the transition period and

general election to the financial crisis coincided with eight of nine of the

financial crises that occurred. Mei and Guo (2004) also found that general

elections were a major factor of the financial crisis because of increases in

market volatility and political uncertainty. Similarly, Vaaler et al. (2005) argue

that general elections in developing countries increase the investment risk

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premium that substantially influences the price and availability of capital in

these countries. Therefore, the increase in risk premium will boost capital

outflows. In addition, Table 5.6 indicates that short-term capital flows relative

to GDP is a dominant factor to determine the probability of a crisis in Indonesia,

with the increase of this variable by 1% increasing the probability of a crisis by

almost 10%. Similar results were also obtained by Ito (1999) who studied the

movement of capital in Asia. He argued that unlike Thailand and Korea, the

movement of capital flows in Indonesia cannot be explained without

considering the political and social shocks.

Another factor that might have caused these two models to send more false

alarms during this period, particularly in 2008, was the existence of the sub-

prime mortgage crisis in USA that sparked the Global Financial Crisis that hit

many countries around the world. Unlike the 1997 Asian Financial Crisis, the

impact of this crisis in Indonesia has been relatively weak (Basri and Rahardja,

2010). However, this crisis might affect the perception of investors, which in

turn might encourage capital flight (Basri and Rahardja, 2010, Titiheruw et al.,

2009), which raises the probability of a crisis for both models. This was shown

in a significant stock index fall from 2830 on 9 January 2008 to 1155 on 20

November 2008, by a credit default swap (CDS) that rose sharply from 250 in

early 2008 to 980 bps in November 2008, and also by an increase in the yield of

government bonds from 10 to 20%. The impact of the latter can be appreciated

when noted that an increase of 1% caused an additional cost to government

debt of Indonesian rupiah 1 trillion (Ministry of Finance of Republic of

Indonesia, 2010).

5.4.4. The Probit EWS Model’s Performance Evaluation

In this subsection, the study evaluates the performance of both models in

predicting the currency crises in Indonesia, either within sample or out-of-

sample. Furthermore, this study employs the same evaluation methods used in

Chapter 4. Thus the results of this evaluation can be used to compare the

performance of all models developed in this study, for example, the signal

approach in Chapter 4 and the artificial neural network model in Chapter 6, and

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later in Chapter 7. For this evaluation, this study set four cut-off probabilities

(or, Pr*), namely 20%, 30%, 40% and 50%, so that a crisis will happen when the

probability of a crisis of these two models exceeds these thresholds, otherwise

there will be no crisis.

In terms of the ability to predict the whole observation, either for crisis or

tranquil periods, both models show positive performance, as they are able to

predict more than 88% (model 1) and 87% (model 2) at Pr*=20% and show a

further increase to 89% (model 1) and 90% (model 2) when Pr* = 50%. But for

the out-of-sample period especially from 1996 to 1998, both models show poor

performance because they are only able to predict around 40%, and when Pr*

was increased to 50%, the ability of both models decreased to above 30%.

During this period, model 1 was slightly better than model 2. However, when

the evaluation period was extended to 2008, model 2 was slightly better than

model 1, with Pr* being higher than 40%.

As for the ability to predict the time of crisis, both models predict the in-sample

currency crises quite well, for their prediction reaches around 94% with

Pr*=20%, although their prediction power drops following a further increase in

Pr*. When Pr*=50%, they are only able to predict 75% (model 1) and 76% (model

2). In contrast, their ability to predict the out-of-sample currency crises reached

53% at Pr*=20%, and as Pr* increased to 50%, their prediction power decreased

to 43% (model 1) and 40% (model 2). On the other hand, model 2 was slightly

better than model 1 in predicting the tranquil periods for both within and out-

of-sample but both models failed to capture any tranquil period between 1996

and 1998.

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TABLE 5.7 The Probit Model’s Performance Evaluation

Note: Pr*: Cut-off probability, M1 refers to the general model or model 1, M2 refers to the specific model or model 2

Regarding the number of false alarms, these results confirm that these two

models perform much better in predicting the in-sample currency crises

compared to the out-of-sample period. For example the number of in-sample

false alarms was around 30% at Pr*=20%, decreasing to 20% at Pr*=50%.

However, the out-of-sample false alarms reached around 80% at Pr*=20% and

dropped slightly to 77% at Pr*=50%. Regarding the number of false signals sent

by both models during the in-sample period, model 1 sent more false alarms

than model 2, and vice versa in the out-of-sample period, where model 2 sent

more false alarms compared to model 1. These results are in accordance with

that shown in Figure 5.2 where the number of false signals from both models

increased in the last decade.

Pr* Assessment Methods

In-sample Out-of-sample

1971-1995 1996-1998 1996-2008

M1 M2 M1 M2 M1 M2

20%

% of observations correctly called 88.67% 87.33% 44.44% 44.44% 48.37% 46.41%

% of crisis periods correctly called 94.44% 94.44% 53.33% 53.33% 53.33% 53.33%

% of tranquil periods correctly called 86.84% 85.09% 0.00% 0.00% 47.15% 44.72%

% of false alarms of total alarms 30.61% 33.33% 27.27% 27.27% 80.25% 80.95%

QPS 0.2267 0.2533 1.1111 1.1111 1.0327 1.0719

GSB 0.0150 0.0200 0.0988 0.0988 0.2222 0.2491

30%





QPS 0.2000 0.2000 1.2222 1.2222 0.9412 0.9673

GSB 0.0044 0.0032 0.1543 0.1543 0.1367 0.1507

40%





QPS 0.1733 0.2067 1.2222 1.2778 0.8758 0.1961

GSB 0.0014 0.0000 0.1543 0.1867 0.1047 0.0769

50%





QPS 0.2133 0.2000 1.2778 1.3333 0.7843 0.7712

GSB 0.0004 0.0004 0.1867 0.2222 0.0578 0.0452

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Furthermore, the performance of these models can also be seen by their level of

accuracy and calibration as indicated by the QPS and GSB statistics in Table 5.7.

At Pr*=20%, model 1 is more accurate than model 2 for both in-sample and out-

of-sample predictions, as well as Pr* which increased up to 40%. However,

when Pr*=50%, model 2 is slightly more accurate than model 1, except for the

out-of-sample from 1996 to 1998.

Given that the purpose of this study is to build a model that has the ability to

predict the currency crises in the future, when deciding which model is the best,

this study focuses on the ability of the model in predicting the crisis period

compared to the tranquil period. In addition, the out-of sample prediction

results are preferred to the in-sample predictions. Recall that based on the

model’s currency crisis definition, the Asian Financial Crisis in 1997/98 was the

only crisis that occurred in the out-of-sample period 1996 to 2008. The extension

of the evaluation period from 1996 to 2008 is only to show the ability of both

models to capture the tranquil periods rather than times of crisis, and to

calculate the number of false alarms sent by these models during the period. So,

based on all the assessment methods utilised during the crisis period (1996-

1998), this study indicates that model 1 is better than model 2, particularly at

Pr*=40% or more.

5.5. Conclusions

This chapter develops the discrete choice probit/logit model, one of the most

popular EWS models to predict currency crises in Indonesia. It applies the

method of evaluation and selection of indicators of the non-parametric model of

the signal approach from Kaminsky et al. (1998). Based on this method, this

study selects 10 of the best variables that cover the same span of time starting

from 1971. Following the study of Kamin et al. (2007), this study initially used

these ten variables for the general model or model 1. It then reduced the

investigation to the specific model or model 2 by dropping the least significant

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explanatory variables from the initial model, until all explanatory variables

were statistically significant.

Based on the regression results, this study finds strong evidence that the short-

term capital flows to GDP, M1 to GDP and loans to deposits ratios, real effective

exchange rates and exports are reliable predictors of currency crises in

Indonesia. Compared with other variables, the short-term capital flows to GDP

ratio is a major contributor in determining the probability of a crisis.

Based on the prediction simulation for both in-sample and out-of-sample

periods, as shown in Figures 5.1 and 5.2, and the performance evaluation in

Table 5.7, both models can predict all the currency crises both within and out-

of-sample periods. However, related to the ability of predicting the out-of-

sample currency crises and the performance evaluation during the Asian

Financial Crisis from 1996 to 1998 in Table 5.7, the general model or model 1

performed better than the specific model or model 2, especially at Pr*=40% or

more. These findings also support that currency crises can be predicted. These

models can be used as EWS models to prevent the occurrence of currency crises

in the future.

Although these models are able to predict the occurrence of currency crises in

Indonesia, it should be noted that in the last decade, these models have sent out

a number of false alarms, particularly during the transition of government and

related political events, especially the general election. This indicates that these

models have difficulty in distinguishing currency crises from political

instabilities.

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CHAPTER 6

PREDICTING INDONESIAN CURRENCY CRISES

USING THE ARTIFICIAL NEURAL NETWORK MODEL

6.1. Introduction

To search for an appropriate early warning system (EWS) model to predict

currency crises in Indonesia, Chapters 4 and 5 explored two standard EWS

models, that is, the signal model and probit/logit model. Previous studies

indicated that these models have been successful in identifying some problems

associated with economic vulnerability, but their results are mixed and not

robust, particularly in predicting currency crises in Indonesia. Nevertheless,

being dominated by linear assumptions, these models are still criticized for

their weaknesses in ascertaining the exact timing of any given crises (Chinn,

2006). In addition, they cannot be completely used as a substitute for the

instinctive judgment that has been widely practiced by policy-makers (Bussiere

and Fratzscher, 2002, Zhuang and Dowling, 2002).

For this reason, many economists and scholars have attempted to find

alternative models. One of the alternative methods available to predict the

currency crises is the artificial neural network (ANN) model. This model has

been used as a successful forecasting model in engineering, financial modeling

and stock market analyses, and in predicting bankruptcy. In addition,

according to Kamruzzaman et al. (2006), the application of the ANN model is

more powerful than the multiple regression models in real life problem solving,

including those found in finance and manufacturing. According to Medsker

(1996), the ANN model has several features that are not available in the

conventional methods, such as fault tolerance, generalization, and adaptability.

In addition, Nag and Mitra (1999) point out that ANN is a non-linear, data-

driven, and self-adaptive model (Fioramanti, 2008) and can be used for

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universal function approximations. Thus, it can independently comprehend the

inherent interrelationships of the given input variables (Hill et al., 1996). It is

also able to deal with erroneous, incomplete or missing, fuzzy or noisy input

data (Kamruzzaman et al., 2006). Furthermore, the ANN model is able to

approximate any continuous function with any desired accuracy (Hornik et al.,

1989, Hornik, 1991). Moreover, the ANN model can be trained to improve its

performance significantly compared to the existing standard EWS models such

as the signal model and discrete choice model.

With these observations in mind, this study applies the ANN model to predict

currency crises in Indonesia. This differs from previous studies in that it focuses

on a single country, Indonesia, and the way that input neurons are selected in

sample periods from 1971 to 2008.

This chapter is organized as follows. Section 6.2 reviews the literature that is

related to the application of the ANN model, while the explanations of the

proposed model are presented in Section 6.3. Section 6.4 presents an application

of an ANN model for Indonesia. The conclusion will be provided in Section 6.5.

6.2. Literature Review on the Application of the ANN EWS Model

ANN models have been widely applied in many fields because of their ability

to solve complex problems. For examples ANN models are commonly applied

in the financial sector for bankruptcy prediction. However, the application of

ANN as an EWS model to predict a currency crisis is still very limited, although

some previous studies have applied an ANN model as an EWS model to

predict currency crises. For example, Nag and Mitra (1999) applied and

compared two models, namely the ANN model and signal approach for

predicting the 1997 currency crises in three Asian countries, Malaysia, Thailand

and Indonesia. For this purpose, they used data from January 1980 to December

1996 for training or developing their model and tested them using the out-of-

sample prediction from January 1996 to January 1998. They found that while

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crises in Indonesia could be predicted, it was more difficult in the case of the

other two sample countries, Thailand and Malaysia. In addition, their signal

model failed to flag the Indonesian crises. In contrast, the ANN model was able

to capture this crisis by sending some alarm signals before the onset of the

crisis.

Peltonen (2006) used and compared two EWS models, namely the ANN model

and probit model to predict the currency crises in 24 emerging economies. The

architecture of his ANN model was composed of three layers with 17 input

neurons, and one hidden layer with two hidden neurons. For the output

neuron, he defined the currency crisis whenever the EMPI passed its threshold

which was the two standard deviations above the index mean. Furthermore he

used the Lavenberg-Marquardt (LM) learning rate instead of back-propagation

in order to accelerate the training process. His results indicated that both

models performed well in predicting the in-sample currency crises but were

found weak for the out-of-sample crises, as they were only able to predict the

Russian crisis of 1998. His models also failed to predict the Indonesian currency

crisis. In general, he found that his ANN model performed better than his

probit model.

Yu et. al (2006) used a general regression neural network1 (GRNN) model to

forecast the 1997/98 currency crisis in four Southeast Asian countries,

Indonesia, the Philippines, Singapore and Thailand. The architecture of their

model was composed of four layers with six input neurons in the input layer,

two hidden layers where the first hidden layer was the pattern layer consisting

of 8 nodes, and the second hidden layer was the summation layer. In turn, this

was divided into two parts, the numerator and the denominator, where both

parts consisted of two nodes and three output neurons in the output layer. For

the input variables, they used the currency exchange price, the rate of change of

1 GRNN is a neural network model which follows the regression model without a model

specification requirement (WALCZAK, S. & CERPA, N. 1999. Heuristic Principle for the Design

of Artificial Neural Network. Information and Software Technology, 41, 107-117.

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price, a ten-day moving stochastic oscillator price, a ten-day moving average, a

ten-day moving variance, and a moving variance ratio. For the output neurons,

they divided the level of the 1997–1998 Southeast Asian currency crises in terms

of the currency exchange rates volatility level into three grades, green, yellow

and red. The model was trained using daily data from 2 January 1997 to 31

December 1998. For out-of-sample prediction, the model was then tested using

the data from 4 January 1999 to 31 December 2004. Their model was able to

predict the crises very accurately when compared to other forecasting methods,

such as the signal model, the logit model, the probit model, and the

discriminant analysis models2, namely linear discriminant analysis (LDA), and

quadratic discriminant analysis (QDA).

In another paper, Yu et al. (2010) applied a multi-scale neural network learning

paradigm to predict the financial crisis for Thailand and Korea. Basically, they

used the standard three layers ANN model with back propagation learning

algorithms. Unlike their previous study, to define their input neurons, they

applied the Hilbert-EMD algorithm to decompose the daily exchange rate series

into ten intrinsic mode components (IMC) for the South Korean Won and seven

IMC’s for the Thai baht. In addition, they defined the currency crisis whenever

the rate of change of the exchange rate passed its threshold of 2.5 standard

deviations above the mean. Their model was then trained using the data set

from 2 January 1996 to 31 December 1997, and tested using the out-of-sample

prediction from 2 January to 31 December 1998.

Similar results to their previous study were obtained, as their proposed model

proved very accurate when predicting crises in both South Korea and Thailand.

In addition, their ANN models were also superior to other forecasting methods,

such as the LDA model, QDA model, the signal model, the logit model, and the

probit model. Among ANN models, their BPNN (Back-Propagation Neural

2 LDA and QDA are the statistical methods for pattern recognition learning. To find out more detail on this discussion about these models, readers can refer to econometric text books such as HAIR, J. F., ANDERSON, R. E., TATHAM, R. L. & BLACK, W. C. 1995. Multivariate Data Analysis, New York, Prentice-Hall.

116

Network) performed better than their GRNN model. These findings indicate

that the ANN model provides a promising model for predicting currency crises.

Franck and Schmied (2003) also applied a multilayer ANN with two hidden

layers to predict the twin crises in Russia and Brazil in 1998/99. They found

that the Russian and Brazilian crises were quite similar to the Asian financial

crisis that hit Thailand, Malaysia, the Philippines and Indonesia, which were

partly caused by the self-fulfilling expectation of foreign investors. Even though

their model was able to predict the onset of the Russian and Brazilian crises,

their results were not overwhelmingly convincing. However, they still believe

that the ANN model is a promising tool of prediction when it comes to

forecasting the onset of a crisis. Similarly, when Fioramanti (2009), applied the

ANN model to predict sovereign debt crises in 46 developing countries from

1980 to 2004, he found that his ANN model outperformed the pooled and the

conditional (or fixed effect) logit model.

These previous studies demonstrate that ANN models can be used as a

promising tool to forecast currency crises or EWS models. This study also

applies an ANN model as an alternative EWS model to predict currency crises

in Indonesia. The approach differs from the previous study in terms of the data

sets used for training, particularly when applying the noise-to-signal ratio from

Kaminsky et al. (1998), which is commonly used in the signal approach for

evaluating and selecting input variables. In addition, this study uses the set of

variables that are statistically significant in the probit model. Apart from

predicting the 1997 crisis, this study also extends the period analysis from 1971

up to 2008 to test whether the developed models are able to identify other crises

that happened during the period 1999 to 2008. A more detailed explanation of

the ANN model will be provided in the following section.

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6.3. Specification of the ANN Model

The application of neural network analysis increased substantially in the 1980s

due to the rapid development in computer technology and progress in the

innovation of new features, such as techniques and algorithms in neural

network analysis itself. However, among ANN models for business analysis,

there are two very successful applications of this model, such as with the

multilayered feed-forward neural network, and the self-organizing map (Smith,

2002). The first represented a neural network with supervised training

(Rumelhart et al., 1986) that can be used for prediction and classification. In

contrast, the self-organizing map represents a neural network with

unsupervised learning (Kohonen, 1982, Kohonen, 1988) that can be used for

clustering.

Basically, the artificial neural network mimics the operation of a biological

neural network. The biological neural system consists of a simple structure that

performs three basic functions: receiving signals (input) from other neurons;

processing these signals; and then sending them to other neurons. Using the

same analogues with this biological neural system, the ANN model mimics the

structure of the biological neurons by connecting all neurons from the input to

output layer.

6.3.1. Architecture of the ANN Model

The architecture of the ANN model can be divided into three layers, as shown

in Figure 6.1. The first layer is known as the input layer, the last layer as the

output layer, and any intermediate layers between the input and output layer

are hidden layers. In addition, each layer in a neural network model consists of

a number of neurons or nodes, and one bias neuron is added, its value always

being 1 for both input and hidden layers. This is because, according to Kecman

(2001), in the logistic and sigmoid activation function, if there is no bias neuron

in a hidden layer, the learning algorithm (that is, back-propagation) cannot

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learn because it can control the shapes, orientation and steepness of all types of

data mapping.

Like a human brain, all neurons in these layers are fully connected to other

neurons in the next layers but there are no connections between neurons within

a layer and each connection is associated with a weight. Basically, in the feed-

forward neural network, all neurons including the bias neuron will send all

signals to other neurons in the next layer in a forward direction.

FIGURE 6.1 Architecture of the ANN Model

Input Layer

In developing the neural network, the first step is to select the appropriate

indicators for input variables to ensure the precision of the prediction of output

- even the type or the numbers of neurons in the input layer are determined by

researchers in relation to their research objectives. However, the selection of

potential independent variables is important (Walczak and Cerpa, 1999, Zhang

et al., 1998). This is because, for a supervised learning neural network, the more

relevant explanatory variables with the output variable will reduce the model’s

learning times. A more detailed discussion about the selection of input neurons

will be presented in the section on empirical findings.

119

Hidden Layer

In Figure 6.1, the next layer or any intermediate layers between input and

output layers are hidden layers. Several issues arise in the hidden layer, such as

the appropriate number of hidden layers and the number of neurons for each

layer. Regarding the number of the hidden layers, in a neural network model,

using more than one hidden layer is also possible. In addition, if there is more

than one hidden layer in the network, the signals from the first hidden layer

will be distributed to the next hidden layer before reaching the output layer,

and this will increase learning time for the model to converge.

Another issue is related to the appropriate number of neurons in a hidden

layer. There is a trade-off between putting too many neurons or too few

neurons in this layer. Using too many neurons will effect a longer learning

period for the model, sometimes leading to the model being over fitted with

data that causes the model to perform poorly when adding new data, because it

starts to model the noise in the data set (Svozil et al., 1997, Walczak and Cerpa,

1999). On the other hand, if the numbers of neurons in the hidden layer is too

small, the neural network will have a problem dealing with a complex data set

(Zhang et al., 1998, Walczak and Cerpa, 1999).

Output Layer

The last layer in Figure 6.1 is the output layer. The output neuron in the output

layer corresponds to the predicted variable. In this study, the expected output is

only one, the probability of a currency crisis in Indonesia having a value in the

range of 0 to 1. Additional information about the output neuron will be

explained in the empirical section. In this model, to guarantee the output

neuron falls into this range, the logistic activation function will be utilized. To

train this model in the supervised learning neural network, this output neuron

will be compared with the target.

120

6.3.2. The Learning Algorithm

As mentioned earlier, like a human brain, the ANN model can be trained to

make its prediction more accurate and powerful. There are many supervised

learning algorithms which are available but the most popular and commonly

used is the back-propagation (Werbous, 1974, Wong et al., 2000). More than 95

percent of the application of neural networks in business apply this learning

algorithm (Wong et al., 2000). As already mentioned, all neurons from the input

to output layer are connected, and each connection has a weight associated with

it. In this supervised learning, as with back-propagation, the objective of the

training process is to discover the appropriate weight for every connection

among neurons in all layers.

According to Fausett (1994), to train a neural network using the back

propagation method can be done in three steps, as follows:

Step 1 – Feed-Forward

In this step, all signals travel from the input to the output layer. Using the feed-

forward mechanisms, all the signals from all neurons in the input layer (Xi)

together with one bias neuron are sent to the neurons in the hidden layer, their

initial weights (vij) being set to small random values. In the hidden neuron, two

processes apply, as seen in Figure 6.2.

FIGURE 6.2 A Single Hidden Neuron

121

After receiving the signals from input neurons, in every hidden neuron, all

these incoming signals are summed using the following equation:

�� = �� +��

(6.1) In this study, the preference output of the neural network model is the

probability of a crisis, which is valued in the range of 0 for “no crisis” to 1 for

“crisis”. For this purpose, this study applies a logistic sigmoid activation

function to keep its value in this range. In order to make it consistent with the

preference output, in this hidden layer, after the summation of all incoming

signals, this value needs to be transformed to any value in the range of 0 and 1

using the same activation function (logistic sigmoid) before sending it to the

next layer, using the following equation:

�� = �� = 11 + exp�−��(6.2)

For simplicity, this study only uses one hidden layer, so these activation signals

(Zj) are sent to the next layer, the output layer. In this output neuron (Yk), a

similar process with a hidden neuron is also applied. First, all incoming signals

from hidden neurons are summed up using the following equation:

�� = �� +��

(6.3)

Then, as the expected value of the output neuron is the probability of a crisis in

the range of 0 to 1, so this summation value will be transformed to any value in

the range of 0 to 1 using a logistic sigmoid activation function:

�� = �(��) = 11 + �� (−��)(6.4)Step 2 - Back Propagation of Error

In this step, the network’s output (Yk) is compared with the target (Ok) and all

the errors from this comparison are back propagated from the output to the

input layer via a hidden layer, as follows:

122

" = 0.5�%&� − ��'(�

(6.5)In the back-propagation mechanism, as the error is a function of weights, then

the network will minimize this error by adjusting the connection weight of each

neuron in the entire layer. By use of the chain rule,

)")�� =))�� *0.5�%&� − ��'(

�+ = −%&� − ��'�,(��)��(6.6)

Where: f’ is the derivative of the activation function.

To make it more convenient, this study defines δk as the portion of the error

correction weight adjustment for wjk, or

-� = %&� − ��'�,(��)(6.7) Using Equation 6.7, Equation 6.6 can be rewritten as follows:

)")�� = −-�� (6.8) For weights connecting the hidden unit (Zj) to the input unit (Xi):

)")�� = −�%&� − ��' ))��(6.9)

= −�%&� − ��'�′(��)�

))�� Using Equation 6.7, Equation 6.9 can be rewrite as follows:

)")�� = −�-��

))�� (6.10)

= −�-�� ))��

= −�-�� ′��

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To make it more convenient, this study defines δj as the portion of the error

correction weight adjustment for vij,, or

-� =�-�� ,��

(6.11) Using Equation 6.11, Equation 6.10 can be rewritten as follows:

)")�� = −-��(6.12) Step 3 – Adjusting the Associated Weights

Finally, the associated weight involved in each connection among neurons in all

layers is gradually adjusted. As already mentioned, in the back-propagation

mechanism, the error will be adjusted gradually in a backward direction. So,

first, the model updates the weights on connections between output neurons to

hidden neurons. After that, this model updates the connection weight between

hidden to input neurons. Basically, the new connection weight (��∗ ) between

output and hidden neuron can be updated through the summation of the old

connection weight (wjk) with the weight correction term (∆wjk), or

��∗ = �� + ∆��(6.13) In addition, the back-propagation algorithm is the optimizing technique using

the steepest descent algorithm that requires a step size or learning rate (α) to be

specified (Zhang and Berardi, 2001), so the weight correction term (∆wjk) can be

obtained using the following equation:

∆�� = −4 )")�� (6.14) In this case, the error as function of weight means that the error will be

minimized by adjusting the connection weight (Fausett, 1994) and the minus

sign (-) in a direction in which the function decreases more rapidly.

Using Equation 6.8, this (Equation 6.14) can be rewritten as:

124

∆�� = 4-�� (6.15) Gradient descent in a back-propagation algorithm which faces the problem of

slow convergence (Svozil et al., 1997, Tang et al., 2006, Peltonen, 2006). To deal

with this problem, Tang et. al (2006) introduced the momentum factor (β),

which is a positive constant between 0 and 1 in the above equation. Using

momentum, it can increase the convergence speed and also avoid a local

minimum because it can smooth the connection weight (Torii and Hagan, 2002),

and when combined with the learning rate parameter, it can also improve the

performance of the gradient descent significantly (Bishop, 1995, Lek et al., 1996).

Thus, the correction term to update the weight for hidden unit (Zj) is

∆��(5) = 4-�� + 6∆��(5 − 1)(6.16) where t denotes the iteration number.

The correction term to update the weight of a biased unit in the hidden layer

will be:

∆�7�(5) = 4-� + 6∆�7�(5 − 1)(6.17) After updating the weight from the output neuron to the hidden neuron, the

next step is to update the connection weights between hidden and input units.

Similarly, new connection weights between hidden and input neurons (��∗ ) can be updated through the summation of the old weight (vij) with the weight

correction term (∆vij), or

��∗ = �� + ∆�� (6.18) Then, the proportion of the change in weight can be obtained using the

multiplication of the learning rate (α) with the derivation of weigh update, or

∆�� = −4 )")�� (6.19) Using Equation 6.12, Equation 6.19 can be rewritten as

125

∆�� = 4-��(6.20) By adding the momentum factor, the correction term to update the weight from

the hidden unit to the input unit is as follows:

∆��(5) = 4-�� + 6∆��(5 − 1)(6.21) The correction term to update the weight for the bias unit in the input layer will

be

∆�7�(5) = 4-� + 6∆�7�(5 − 1)(6.22)

This learning process will be continued until the network meets one condition

whenever the net output of the model converges to its target, or the minimum

threshold of the error is achieved. However, if the neural network never

converges to its target, setting the maximum number of iterations can stop this

learning process. So, even though this model fails to achieve its target, that is,

the minimum error, this learning process will stop whenever the maximum

number of iterations is achieved. The application of this model to predict the

Indonesian currency crises will be discussed in the next section.

6.4. The Application of the ANN EWS Model to Predicting


6.4.1. Constructing the ANN Model

In building the ANN model to predict currency crises in Indonesia, there are

several important steps. The first step is to determine the output node. Selecting

the input neurons and determining the number of hidden layers and hidden

neurons will then follow. The next step will be to determine the type of

activation function, the maximum number of iterations, the learning rate, as

well as the momentum rate.

126

Similar to previous chapters, in this section, the output neuron is the currency

crises3 in Indonesia. To determine the currency crises, this study follows the

study of Kaminsky et al. (1998) that defines the currency crisis as a speculative

attack against the local currency followed by a sharp decline in the exchange

rate, or significant decrease in foreign reserve, or the combination of both.

Empirically, a currency crisis occurs when the exchange market pressure index

(EMPI) passes its threshold that is three times its standard deviation above the

index’s mean.

The next step is to select the set of variables for the input neurons to ensure the

precision of the prediction of output. Although the ANN model can be trained

to improve its performance, according to Walczak and Cerpa (1999); Zhang et

al. (1998), the selection of input variables is very important and greatly affects

the outcome of the ANN model. However, until now, no particular methods

have been dedicated to select the input variables in this model. For example, Yu

et al. (2006) used the modification of local currency to predict the speculative

attacks on domestic currency, although in their subsequent studies, to define

their input variables, Yu et al. (2010) used the intrinsic mode components (IMC)

of two local currencies, South Korea’s Won and Thailand’s Baht, using the

decomposition method, the Hilbert-EMD algorithm.

Unlike the studies of Yu et al. (2006, 2010), this study employs the noise-to-

signal ratio for selecting a set of input variables. This is commonly used in the

signal approach to evaluate performance and to choose the set of leading

indicators. Based on this method, this study chooses the top ten variables,

which are similar to those used for the application of the probit model in

previous chapter. This model is later referred to as a “general model” or “model

1”. Table 6.1 presents the list of input variables for model 1. Thus the

performance of both models can be compared later on in Chapter 7 because the

ANN model without a hidden layer is quite similar to the discrete choice model

as seen in the probit or logit models (Tu, 1996) and also in many other studies 3 See Chapter 3 for a more detailed discussion about the currency crisis dating system.

127

that compare the performance of these models (Yu et al., 2006, Yu et al., 2010,

Tu, 1996, Dreiseitl and Ohno-Machado, 2002, Ottenbacher et al., 2004, Peltonen,

2006).

TABLE 6.1 List of Input Nodes for Model 1

No Description NSR

01 Real US$/yen exchange ratea 0.03

02 Short-term capital flows to GDP 0.03

03 US annual growth rate 0.06

04 US real interest ratec 0.09

05 US real interest rate 0.13

06 Loans to depositsc 0.23

07 M1 to GDPc 0.23

08 Real effective exchange ratea 0.24

09 Exportsb 0.29

10 M1 to GDP 0.32 Note: a deviation from trend-HP filter, b 12 months percentage change, c 12 months change.

The number of input variables used in the ANN model is also important in that

it affects the outcome of the ANN model (Walczak and Cerpa, 1999, Zhang et

al., 1998). Several studies indicate that using fewer input variables is superior

than using too many input variables because the inclusion of noise in the data

set degrades the performance of the ANN models (Walczak and Cerpa, 1999,

Yu et al., 2010). Opposite results were found by Jain and Nag (1995), the

performance of their ANN model using many input neurons being superior to

their model using fewer input neurons. Therefore Walczak and Cerpa (1999)

concluded that a reduction in the number of input neurons must be done

carefully to ensure that only variables having noise or correlated input neurons

are discarded so as to avoid performance degradation.

Taking these findings into consideration, this study contributes to the debate

because it also develops and compares two ANN models, namely the “general

model” or “model 1” and “specific model” or “model 2”, which has fewer input

neurons. As with Walczak and Cerpa (1999), in selecting the input variables for

128

model 2, this study use five variables4 which are statistically significant in the

probit model applied in Chapter 5. For consistency and comparability,

following the study of Yu et al. (2010), this study, adopting model 2, uses the

same number of hidden neurons and training parameters as model 1. The list of

these input variables is presented in Table 6.2.

TABLE 6.2 List of Input Nodes for Model 2

No Description

1 Short-term capital flows to GDP

2 Loans to depositsc 3 Real effective exchange ratea 4 Exportsb 5 M1 to GDP

Note: a deviation from trend-HP filter, b 12 months percentage change, c 12 months change

Moreover, Walczak and Cerpa (1999) pointed out that the presence of

correlation among the input variables increased the noise that led to decreased

performance of the ANN models. Accordingly, based on the correlation matrix

as presented in Table 5.2, Chapter 5, this study found that there is no

multicorrelinearity among the input variables. In addition, as the dimensions of

these data sets are varied, and in order to provide equal proportional

contributions as well as to remove biases in the forecasting model, these data

are normalized before being added to the model (Hall et al., 2009). Furthermore,

according to Hamid (2004), data normalization also provides equal statistical

distribution among all input and output variables. For this purpose, and

following Hall et al. (2009), before putting these variables and input neurons in

the input layer, all data will be normalized to keep values in the range -1 to 1,

using the following equation:

�8 = 2 9 �8 −:;<(�8):=�(�8) − :;<(�8)> − 1(6.23)

4 From the general model, the least statistically significant explanatory variables were

eliminated one by one until all variables were statistically significant. For more detail, see

Chapter 5.

129

In addition, this study also adds one bias neuron, the value of which is always

one in the input layer for both models.

The next step is to determine the optimum number of hidden layers and the

number of hidden neurons for each hidden layer. In the ANN model with back-

propagation learning algorithm, according to Walczak and Cerpa (1999), it is

more flexible to determine the number of hidden layers. Even though in the

ANN model, using more than one hidden layer is also possible, many scholars

argue that one hidden layer is sufficient for the model to solve almost all

problems (Swales and Yoon, 1992, Hornik et al., 1989, Yu et al., 2007, Dreiseitl

and Ohno-Machado, 2002, Svozil et al., 1997). With regard to the number of

hidden layers, although initially using a single hidden neuron, this study also

tested this model by adding another hidden layer to see whether this additional

hidden neuron could improve the performance of the model or not. However,

similar to the study of Baum and Hassler (1989) it has been found that

additional hidden layers degrade rather than improve performance of the

model, as its error increased. Moreover, the cost of adding another hidden layer

also increases the learning time. This is because the additional hidden layer can

lead to a model trapped in a local minima, with instability of gradient descent,

so that the learning process becomes slower and needs more time (Svozil et al.,

1997).

As previously noted, one of the weaknesses of this model is a tendency to over-

fit, a tendency that increases in line with an increase in the number of hidden

neurons, which in turn lowers the ability of the ANN model (Svozil et al., 1997,

Walczak and Cerpa, 1999). However, too few hidden neurons decrease the

ability of the model to deal with a more complex data set (Zhang et al., 1998,

Walczak and Cerpa, 1999). To date, there is no agreement as to the optimal

number of hidden neurons that should be used, it being common practice to

choose an arbitrary basis in order to maximize the performance of the model or

to minimize the error (Yu et al., 2010, Zhang et al., 1998). For example, some

have claimed that the maximum number of hidden neurons should be equal to

130

the input neurons, while others claim half the number is enough, while others

recommend two-fold, or even a doubling plus 2.

While there is a wide spectrum of opinions, this study followed the advice of

Song (2010) by applying the specific-to-general method when determining the

optimal number of hidden neurons. Furthermore, the ANN model adopted

uses a variety of hidden neurons ranging from one to 22 hidden neurons, the

result of which is presented in Figure 6.3. This has allowed the optimal number

of hidden neurons to be chosen at the point where the model has the smallest

training error. Consequently, this study found that the smallest training error

was obtained when the number of hidden neurons equaled the number of input

neurons, a finding similar to the study of Chakraborty et al. (1992) and Tang

and Fishwick (1993). This figure indicates that the ANN model with the number

of hidden neurons less than the input number, that is, 10 neurons, limits the

ability of the model to deal with complex and non-linear data sets (Zhang et al.,

1998, Walczak and Cerpa, 1999). However, Svozil et al. (1997) and Walczak and

Cerpa (1999) warn that using too many hidden neurons causes the model to be

over-trained, thus potentially degrading the performance of the model because

its error increases in line with increasing the number of hidden neurons used

(see Figure 6.3). It should be noted that this study also adds one bias neuron,

the value of which is always one in the hidden layer.

As mentioned in the earlier section on methodology, this study uses the logistic

activation function in the hidden and output neurons. This is because the target

of the output neuron in these models is the probability of a crisis, which is

valued between 0 and 1. To ensure the output of these models is within this

range, this study applies the logistic activation function for the output neuron,

and for consistency, adopts the same activation function, the logistic activation

function being used in both models for every hidden neuron.

131

FIGURE 6.3 Numbers of Hidden Neurons vs. RMS Errors

In the ANN model with a back-propagation learning algorithm, there are many

factors that determine model performance. Other than those described above,

the performance of these models is determined by the number of iterations, the

value of momentum and the learning rate. Similar to Yu et al. (2010), in this

study, these factors were carried out by trial and error until the optimum

performance of these models was obtained.

The number of iterations used in this model is determined by trial and error.

Therefore in developing this ANN model, simulations were carried out with

different numbers of iterations to achieve the smallest training error, such as

10,000 (0.0971), 20,000 (0.0812), 30,000 (0.0622), 40,000 (0.0622) and 50,000

(0.0628). These simulation results indicate that although the iteration of 30,000

and 40,000 has the same error rate, higher iteration rates require more time for

convergence or training. Based on this argument, this study found that the most

efficient iteration number was 30,000.

Likewise, trial and error was utilized to determine the optimal learning rate and

momentum rate. According to Tang et al. (1991), a high learning rate is more

suitable for a less complex model, however, the combination of low learning

0,174

0,101

0,085

0,081

0,071

0,0630,066

0,064 0,0640,062

0,076

0,0710,075

0,0670,065

0,069 0,068

0,077

0,0720,068

0,0700,066 0,066

0,05

0,07

0,09

0,11

0,13

0,15

0,17

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 19 20 21 22

RM

S e

rro

rs

Numbers of hidden neurons

132

rate and high momentum rate is more suitable for a complex model. Based on

their argument, this study sets the learning rate at 0.01 and the momentum rate

at 0.8 and found the training error to be equal to 0.0622. This study also tested

the model by raising the learning rate to 0.1 and 0.3 but found the training

errors increased to 0.1486 and 0.4365, respectively. Likewise, when the lower

momentum rate was adopted, the training error increased to 0.0765 and needed

a much longer training time. This simulation confirms the finding of Tang et al.

(1991). Finally, according to Lek et al. (1996), using the optimum combination of

learning rate and momentum rate can speed up the training time and at the

same time prevent the model from becoming trapped in a local minimum.

As previously mentioned, in predicting the Indonesian currency crises, this

study applies the ANN model with the supervised learning back-propagation

algorithm. This is because back-propagation is quite a popular method

(Werbous, 1974, Wong et al., 2000), particularly for business and finance. In

addition, based on the study of Yu et al. (2010) it was found that the ANN

model with back-propagation performed better than the general regression

neural network (GRNN). In addition, poor results were achieved by Peltonen

(2006) when using the ANN model with the Lavenberg-Marquardt (LM)

algorithm instead of back-propagation.

Towards this purpose of training, this study, divides the sample period into

two sub-samples, being the in-sample period from January 1971 to December

1995, and the out-of-sample period from January 1996 to September 2008. In

order to improve the performance of both models, these ANN models will be

trained using the in-sample data, and after that both models will be tested using

the out-of-sample data set. Following this, by using the training parameters

mentioned in Table 6.3, both models will be trained. First, both models set their

initial connection weights between neurons and bias neurons in the input layer

with the hidden neuron in the hidden layers selected randomly. Using the feed-

forward, these signals always propagate toward the forward direction, from

input layer to output layer via the hidden layer.

133

Furthermore, in this hidden neuron, these models sum all these signals and

transform them in the range of 0 to 1 using the logistic activation function. In

this hidden layer, these models also randomly set the initial connection weight

between the hidden neuron and the bias neuron to output neuron. These

models then send transformed signals from the hidden to the output neuron

and a similar process also applies. All these signals are summed and using the

logistic activation function, will convert to within 0 to 1.

TABLE 6.3 Elements of Artificial Neural Network Architecture

No Training Information Model 1 Model 2

1 Type of network Multi-layer perceptron Multi-layer perceptron

2 Number of layers 3 3

3 Number of hidden layer 1 1

4 Number of input neurons 10 5

5 Number of hidden neurons 10 10

6 Number of output neurons 1 1

7 Activation functions Logistic Logistic

8 Performance function Mean squared error Mean squared error

9 Training algorithm Back-propagation Back-propagation

10 Starting weights and biases Random Random

11 Number of iterations 30000 30000

12 Training error 0.062 0.121

13 Learning rate (α) 0.010 0.010

14 Momentum factor (β) 0.800 0.800

The learning stage begins by comparing the output neuron with its target. This

error is then transferred in a backward direction from output layer to input

layer via the hidden layer using the back-propagation learning algorithm. This

learning process aims to determine the appropriate connection weights for all

neurons in both models. This study sets 30,000 as the maximum number of

iterations for both models. As a result, these models will stop the learning

process whenever these models reach the minimum error or the maximum

number of iterations. After the iterations reach 30,000, the learning error is

0.062231 (model 1) and 0.11911 (model 2). The final weights and biases for all

neurons in all layers are presented in Table A6.1 and A6.2 (model 1) and Tables

A6.3 and A6.4 (model 2).

134

In addition, based on these training processes, this study is able to indicate the

average contribution of each input neuron to the output neuron for both

models. Table 6.4 presents the contribution of input neurons to the output

neuron for model 1. From this table, the most significant contributor is the real

effective exchange rate, followed by the 12-month percentage change of loans to

deposit, and the 12-month percentage change of the US real interest rate. For

more detail, see Table 6.4.

TABLE 6.4 Average Contribution of Input Nodes to Output Node for Model 1

No Description Contribution

1 Short-term capital flows to GDP 8.64%

2 Exportsb 8.59%

3 Real effective exchange ratea 17.40%

4 M1 to GDP 6.93%

5 M1 to GDPc 7.28%

6 Loans to depositsc 13.11%

7 US real interest rate 10.55%

8 US real interest ratec 10.84%

9 US annual growth rate 6.94%

10 Real US$/yen exchange ratea 9.72% Notes: a deviation from trend-HP filter, b 12 months percentage change, c 12 months change.

Furthermore, Table 6.5 presents the contribution of input neurons to the output

neuron for model 2. Based on this table, and similar to model 1, the main

contributor to the output neuron for model 2 is also the real effective exchange

rate. This is then followed by the 12-month change of loans to deposit and the

12-month change of exports.

TABLE 6.5 Average Contribution of Input Nodes to Output Node for Model 2

No Description Contribution

1 Short-term capital flows to GDP 14.06% 2 Exportsb 18.95% 3 Real effective exchange ratea 29.22% 4 M1 to GDP 14.16% 5 Loans to depositsc 23.61%

Notes: a deviation from trend-HP filter, b 12 months percentage change, c 12 months change.

135


In this section, both models are used to simulate and evaluate their ability to

predict the probability of the Indonesian currency crises, as presented in Figure

6.4 for the in-sample prediction (1971-1995), and Figure 6.5 for the out-of-

sample prediction (1996-2008). In these figures, the black solid line is the

probability of a crisis for model 1 (ann_M1) and the red thin solid line is the

probability of a crisis for model 2 (ann_M2). In addition, as this study uses the

24-month of crisis window, so the yellow shaded areas are the 24 months prior

to the currency crises.

In-Sample Prediction

As earlier noted, during this period, based on the model’s currency crisis

calculation, Indonesia experienced three currency crises, namely November

1978, April 1983 and September 1986. In Figure 6.4, it is shown that both models

are able to predict these crises within 24 months prior to the crises. For example,

in the first crisis, model 1 sent warning signals with a probability of a crisis at

11% in September 1976, increasing to 36% in December 1976 (or 24 months prior

to the crisis), then jumping to 96% in the following month and to 100% in March

1977 where it remained until the crisis occurred in November 1978. Model 2

sent warning signals from August 1976 when the probability of a crisis reach

73%, reducing gradually to 17% before increasing to 48% in December 1976,

some 24 months prior to the crisis. After that, its probability of a crisis increased

to 100% and although thereafter it fluctuated, overall it remained high until the

crisis date.

For the second in-sample currency crisis, both models sent warning signals

from April 1981, with the probability of a crisis reaching 30% (model 1) and 63%

(model 2). After this the probability of a crisis for both models increased to

around 80% in the 24 months prior the crisis, and a further increase to 100% in

July 1981 (model 1) and June 1981 (model 2). With the exception of September

1981 when the probability of a crisis dropped to 83% (model 1) and 63% (model

2), their probability of a crisis

when the percentages dropped

FIGURE 6.4 The ANN Models: In

In predicting the third currency crisis,

warning signals about the occurrence of this crisis,

probability of a crisis in both models

probability of a crisis of model 1

crisis for model 2. In the 24 months p

signals with the probability of

continued to increase to 100% in January 1985. However

probability of a crisis was very high until the crisis date whe

dropped to 56%. Unlike the first model,

warning signals (thus, more tha

probability of a crisis, although fluctuating,

move positively until reaching the highest peak

gradually decreasing to 77%

during the following month.

25% in January 1986, increased

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

19

71

M0

1

19

71

M1

1

19

72

M0

9

19

73

M0

7

19

74

M0

5

19

75

M0

3

19

76

M0

1

19

76

M1

1

19

77

M0

9

19

78

M0

7

19

79

M0

5

cc24m

136

s remain high until a month prior to the crisis date

dropped to 78% (model 1) and 59% (model 2).

The ANN Models: In-Sample Prediction

In predicting the third currency crisis, although both models were able to send

warning signals about the occurrence of this crisis, unlike the first two crises the

both models was relatively high. However,

crisis of model 1 was much higher than the probability of

. In the 24 months prior to the crisis, model 1 sent

ls with the probability of a crisis at 67%. After that, although fluctuating,

100% in January 1985. However, on ave

s very high until the crisis date when its probability

Unlike the first model, in May 1985, model 2 began to send

more than 24 months prior to the crisis)

, although fluctuating, reaching 58%. It then tended

until reaching the highest peak at 83% in February 1985,

to 77% in May 1985, before reaching its apex

the following month. Thereafter it continued to fluctuate, dropped

, increased to 55% in February 1986, and fluctuated

19

79

M0

5

19

80

M0

3

19

81

M0

1

19

81

M1

1

19

82

M0

9

19

83

M0

7

19

84

M0

5

19

85

M0

3

19

86

M0

1

19

86

M1

1

19

87

M0

9

19

88

M0

7

19

89

M0

5

19

90

M0

3

19

91

M0

1

19

91

M1

1

19

92

M0

9

19

93

M0

7

cc24m ann_M1 ann_M2

the crisis date

re able to send

two crises the

was relatively high. However, the

much higher than the probability of a

rior to the crisis, model 1 sent warning

although fluctuating, it

n average its

its probability

n to send

n 24 months prior to the crisis) when its

%. It then tended to

% in February 1985, then

its apex at 99%

dropped to

and fluctuated around

19

93

M0

7

19

94

M0

5

19

95

M0

3

137

70%, before dropping to 7% in September 1986. Since that time, the probability

of a crisis for both models has tended to fluctuate but movements have not been

significant.

Out-of-Sample Prediction

As noted in the beginning of this chapter, this study only captures one out-of-

sample currency crisis, the Asian financial crisis of 1997-98. To test the

performance of these EWS models in predicting the out-of-sample currency

crisis, this study presents the time series probability of a crisis for both models

in Figure 6.5, from January 1996 to September 2008. Both models will be

evaluated in terms of their ability to predict the Asian financial crisis and as

illustrated, both are able to predict the occurrence relatively well. Furthermore,

in general, the probabilities of a crisis in both models move in the same pattern

for the entire sample period.

In predicting this crisis, both models start sending warning signals from

January 1996 with the probability of a crisis about 66% (model 1) and 9%

(model 2). Their probability of a crisis then increases gradually to 100% in July

1996 and remains there for about six months before dropping to around 70% in

January 1997. Although in June 1997 the probability of a crisis increases to 95%

(model 1) and 99% (model 2), their probability of a crisis falls back dramatically

to 6% (model 1) and 0% (model 2) before rising again sharply to 100% in

January where it remains to June 1998 for model 1 and December 1999 for

model 2. Unlike model 2, model 1 did not send any warning signals for the next

three months with its probability of a crisis remaining low from July to

September 1998. After that time its probability of a crisis rises back to 100% in

November 1998, remains high until December 1999 and thereafter decreases.

However, unlike model 2, model 1 decreases gradually and remains low until

March 2001.

Thereafter, both models still send warning signals, as their probability of a crisis

tends to fluctuate. For example, their probability of a crisis increases to 100%

from April 2001 to March 2002 except during three months (July to September

2001) when their probability drop

send warning signals from October to December 2004 but their probability of

crisis at around 30% is not to

models again send warning signals. For example,

probability of a crisis in model

from 2008, its probability of a crisis increase

2008 where it remained until the end of sample period in September 2008. In

contrast, from the end of 2006 to 2007, model 2 sen

compared to model 1, with its

51%. Entering 2008, model 2 start

probability of a crisis around

September 2008.

FIGURE 6.5 The ANN Models: Out

Compared to the in-sample prediction presented in Figure 6.4, the out

sample prediction presented in Figure 6.5 clearly indicates that even though no

currency crisis has occurred since the Asian F

models have continued to send more signals over the last decade. Therefore,

these signals sent by both models can be categorized as false signals since these

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

19

96

M0

1

19

96

M0

7

19

97

M0

1

19

97

M0

7

19

98

M0

1

19

98

M0

7

19

99

M0

1

19

99

M0

7

20

00

M0

1

cc24m

138

when their probability drops significantly. Furthermore, both models also

warning signals from October to December 2004 but their probability of

not too significant. From the end of 2006 to 2008

warning signals. For example, during this period, the

model 2 fluctuated but not too significantly, however,

crisis increased significantly to reach 100% in June

until the end of sample period in September 2008. In

the end of 2006 to 2007, model 2 sent more warning signals

probability of a crisis fluctuating at an average of

%. Entering 2008, model 2 started sending warning signals in April,

crisis around 52%, and continued to increase to 100% in

The ANN Models: Out-of-Sample Prediction

sample prediction presented in Figure 6.4, the out


s has occurred since the Asian Financial Crisis in 1997/98, both

send more signals over the last decade. Therefore,


20

00

M0

1

20

00

M0

7

20

01

M0

1

20

01

M0

7

20

02

M0

1

20

02

M0

7

20

03

M0

1

20

03

M0

7

20

04

M0

1

20

04

M0

7

20

05

M0

1

20

05

M0

7

20

06

M0

1

20

06

M0

7

20

07

M0

1

20

07

M0

7

cc24m ann_M1 ann_M2

rthermore, both models also

warning signals from October to December 2004 but their probability of a

to 2008, both

during this period, the

, however,

100% in June

until the end of sample period in September 2008. In

more warning signals

average of

April, with the

and continued to increase to 100% in

sample prediction presented in Figure 6.4, the out-of-


risis in 1997/98, both

send more signals over the last decade. Therefore,


20

07

M0

7

20

08

M0

1

20

08

M0

7

139

transmitted signals were not followed by any currency crisis within a 24-month

period.

To explain why these models send false alarms, particularly when using the

out-of-sample is quite problematic. Unlike the parametric approach for which it

is possible to identify the marginal effect of each variable relative to the

dependent variable and which makes possible identification of the main factors

causing the model to issue many false alarms, by analyzing the movement of

the main contributor variables this problem is resolved. On the other hand, one

major disadvantage of the ANN model is its “black box” nature, which leads to

this model having limitations in explaining the causal relationship between

input and output (Tu, 1996). Although, Tables 6.4 and 6.5 illustrate the

contribution of each input neuron to output neuron, outside these input nodes,

there are still many factors that affect the value of the output node, such as the

numbers of hidden layers and hidden nodes used, and the values of learning

rates and momentum, plus the number of iterations used.

6.4.3. The ANN EWS Model’s Performance Evaluation

This section has attempted to evaluate the performance of these two models

based on their ability to predict both in-sample and out-of-sample currency

crises in Indonesia. As mentioned earlier, the in-sample evaluation has been

based on the ability of these models to capture the three currency crises within

the 24 crisis windows, namely November 1978, April 1983 and September 1986.

As described before, from January 1996 to September 2008, Indonesia had only

one currency crisis, the Asian financial crisis, which occurred in 1997/98.

Therefore, this study will evaluate the performance of both models based on

their ability to predict the Asian financial crisis using the sub-sample from 1996

to 1998. In addition, this study also evaluates their performance for the entire

sample from 1996 to 2008. Evaluation methods used in this chapter are similar

to those methods used in previous chapters. Similarly, in this chapter there are

140

four cut-off-probabilities, namely 20%, 30%, 40% and 50%, and crisis occurs

when the models probability of a crisis exceeds these cut-off-probabilities or

thresholds, otherwise there is no crisis. The evaluation results for both models

can be seen in Table 6.6.

In general, the performance of the EWS model can be seen from the ability of

the model to capture the whole observation in both periods of crisis and

tranquility. As shown in Table 6.6, both models showed very good performance

in predicting the entire in-sample periods, and at Pr*=20%, both models can

capture 98% (model 1) and 94% (model 2). The ability of these models increases

in line with increasing Pr*, for example when Pr*=50%, their performance

increases to almost 100% (model 1) and 96% (model 2). Similar results were

obtained when evaluating the ability of models to predict the out-of-sample

period, both during the Asian financial crisis from 1996 to 1998 and the entire

sample from 1996 to 2008. Unlike the in-sample results where model 1 performs

slightly better than model 2, for the out-of-sample prediction, the ability of

model 1 is much better than model 2. For example, at Pr*=20%, model 1 is able

to predict 89% and 69% (entire sample), while model 2 only captures 69% and

60% (entire sample). When Pr*=50%, during the crisis period, the ability of both

models drops to 83% and 76% (entire sample) for model 1 and 56% and 70%

(entire sample) for model 2.

In detail, the performance of both models can be evaluated based on their

ability to predict the crisis periods, tranquil periods and also the number of

false alarms transmitted. With regard to the ability to predict the 24 month

period before the in-sample crises, at Pr*=20%, both models can predict very

well, being 100% (model 1) and 99% (model 2), but when Pr*=50%, the ability of

both models decreases slightly to 99% (model 1) and 93% (model 2). However,

when predicting the out-of-sample currency crisis, model 1 is more dominant

than model 2 as it can predict 97%, while model 2 is only able to predict 83% at

Pr*=20%. However, when Pr* rises to 50%, the predictive ability of both models

drops to 90% (model 1) and 67% (model 2).

141

TABLE 6.6 The ANN Model’s Performance Evaluation

Pr* Assessment Methods

In-sample Out-of-sample

1971-1995 1996-1998 1996-2008

M1 M2 M1 M2 M1 M2

20%





QPS 0.0333 0.1200 0.2222 0.6111 0.6144 0.7974

GSB 0.0006 0.0057 0.0062 0.0015 0.1730 0.2222

30%





QPS 0.0067 0.0800 0.2778 0.7222 0.5621 0.7190

GSB 0.0000 0.0014 0.0015 0.0015 0.1300 0.1436

40%





QPS 0.0067 0.0533 0.2778 0.8333 0.5098 0.6667

GSB 0.0000 0.0004 0.0015 0.0139 0.1047 0.0930

50%





QPS 0.0067 0.0733 0.3333 0.8889 0.4837 0.6013

GSB 0.0000 0.0000 0.0000 0.0247 0.0821 0.0578

In capturing the tranquil periods, model 1 consistently performs well compared

to model 2 for both periods in all levels of cut-off-probabilities. When Pr*=20%,

the difference in the ability of both models is quite large for both in-sample and

out-of-sample. However, as Pr* increases, the ability of models to capture these

tranquil period increases and their difference becomes smaller, although model

1 does perform better than model 2. This finding is also supported by the

number of false signals relative to all signals sent by both models. Based on this

ratio in Table 6.6, this study finds that model 2 sent more false signals

compared to model 1 for the entire out-of-samples from 1996 to 2008.

142

In addition, the performance of both models can also be analyzed according to

their level of accuracy and calibration. In general, both models have performed

well as indicated by the small values of QPS and GSB, in which the value of

zero indicates the perfect level of accuracy and calibration. Based on these two

measures, overall, model 1 performs better than model 2. This finding is also

consistent with other evaluation methods mentioned in the above tables that

confirm model 1 outperforms model 2 in predicting the Indonesian currency

crises for both in-sample and out-of-sample periods. Furthermore, unlike the

study of Walczak and Cerpa (1999) and Yu et al. (2010), this finding confirms

the results of Jain and Nag (1995), that the ANN model with more input

neurons is better than ANN model using fewer input neurons.

6.5. Conclusions

The application of the ANN model as an EWS model to predict the currency

crisis, particularly in the case of Indonesia, is promising as it is able to predict

almost the 24 months prior to the in-sample currency crises in Indonesia. Even

though the prediction results for the out-of-sample currency crisis are not as

good as the in-sample prediction, however, the ANN model also performs very

well in predicting the 24 months prior to the out-of-sample Indonesian currency

crisis.

Based on the assessment methods, and comparing the performance of both

models, model 1 performs much better than model 2 in its ability to predict the

in-sample and out-of-sample currency crises. The findings also support the idea

that currency crises can be predicted and that the application of the ANN

model in predicting them, particularly for Indonesia, is promising.

The real effective exchange rate and the 12-month change of loans to deposit

ratio are the main contributors for the probability of a crisis for both models.

Model 1 also indicates that the 12-month percentage change of US real interest

rate contributes significantly in determining its output. However, model 2

143

identifies that the 12-month change of exports and the short-term capital flows

to GDP ratio also contribute significantly to the determination of the probability

of a crisis.

144

Appendixes

TABLE A6.1 The Weights and Adjustment Weight from Input to Hidden Layers for Model 1

Layer Weight (v) Weight Delta (∆v)

Input Hidden Symbol Value Symbol Value

1 1 v11 3.79331 ∆v11 0.000056

2 1 v21 -1.50928 ∆v21 0.000108 3 1 v31 -1.20637 ∆v31 - 0.000276

4 1 v41 -0.31833 ∆v41 - 0.000033 5 1 v51 -3.01624 ∆v51 0.000125

6 1 v61 -8.23915 ∆v61 - 0.000118

7 1 v71 10.98769 ∆v71 0.000194

8 1 v81 -2.46586 ∆v81 - 0.000046 9 1 v91 7.37515 ∆v91 0.000053

10 1 v101 -1.72519 ∆v101 0.000120 11 1 v01 -4.29891 ∆v01 - 0.000092

1 2 v12 -0.64360 ∆v12 - 0.000029 2 2 v22 -4.35508 ∆v22 0.000140

3 2 v32 -4.60598 ∆v32 0.000015 4 2 v42 -6.08008 ∆v42 - 0.000100

5 2 v52 2.23115 ∆v52 0.000135

6 2 v62 1.15679 ∆v62 0.000142 7 2 v72 5.15782 ∆v72 - 0.000046

8 2 v82 -9.81534 ∆v82 - 0.000121 9 2 v92 2.90272 ∆v92 0.000013

10 2 v10 2 4.22672 ∆v10 2 - 0.000008

11 2 v02 0.30987 ∆v02 - 0.000065

1 3 v13 0.00664 ∆v13 0.000061

2 3 v23 3.75700 ∆v23 0.000107 3 3 v33 -10.59311 ∆v33 - 0.000081 4 3 v43 -3.06346 ∆v43 0.000004

5 3 v53 -2.75972 ∆v53 0.000082

6 3 v63 -4.09971 ∆v63 - 0.000199 7 3 v73 8.07263 ∆v73 0.000244

8 3 v83 -1.21470 ∆v83 - 0.000145

9 3 v93 6.39143 ∆v93 0.000057 10 3 v10 3 -1.35272 ∆v10 3 0.000022

11 3 v03 -0.81125 ∆v03 - 0.000080

1 4 v14 3.02614 ∆v14 0.000088 2 4 v24 0.23764 ∆v24 - 0.000013

145


(continued)



3 4 v34 -0.03899 ∆v34 0.000037

4 4 v44 -1.63580 ∆v44 0.000007 5 4 v54 -1.71434 ∆v54 0.000046 6 4 v64 0.19454 ∆v64 - 0.000040

7 4 v74 0.86295 ∆v74 0.000003 8 4 v84 -0.59612 ∆v84 - 0.000025

9 4 v94 0.40911 ∆v94 - 0.000066

10 4 v10 4 -0.19716 ∆v10 4 - 0.000010 11 4 v04 1.43076 ∆v04 0.000018

1 5 v15 -1.79507 ∆v15 0.000022

2 5 v25 -0.25624 ∆v25 - 0.000092 3 5 v35 -2.51936 ∆v35 - 0.000135

4 5 v45 1.14238 ∆v45 - 0.000018

5 5 v55 1.90700 ∆v55 - 0.000082

6 5 v65 -0.03742 ∆v65 0.000029 7 5 v75 -2.34246 ∆v75 - 0.000005

8 5 v85 1.06800 ∆v85 0.000001

9 5 v95 -3.58290 ∆v95 - 0.000003 10 5 v10 5 -0.32378 ∆v10 5 - 0.000071

11 5 v05 2.00241 ∆v05 0.000100 1 6 v16 5.05771 ∆v16 0.000018

2 6 v26 4.03198 ∆v26 0.000128 3 6 v36 0.93862 ∆v36 - 0.000010

4 6 v46 2.50219 ∆v46 0.000086

5 6 v56 -6.15359 ∆v56 0.000055

6 6 v66 -6.10206 ∆v66 - 0.000001 7 6 v76 -0.71376 ∆v76 0.000066

8 6 v86 3.57601 ∆v86 - 0.000012 9 6 v96 2.86236 ∆v96 - 0.000030

10 6 v10 6 -3.67082 ∆v10 6 - 0.000048 11 6 v06 1.44063 ∆v06 - 0.000109

1 7 v17 -5.73604 ∆v17 - 0.000056 2 7 v27 6.05849 ∆v27 - 0.000070 3 7 v37 -9.32790 ∆v37 - 0.000248

4 7 v47 -2.86865 ∆v47 0.000005 5 7 v57 -0.42072 ∆v57 - 0.000030

6 7 v67 -6.69436 ∆v67 0.000086

7 7 v77 -1.47556 ∆v77 - 0.000058

146


(continued)



8 7 v87 2.98994 ∆v87 0.000023

9 7 v97 1.22135 ∆v97 0.000055 10 7 v10 7 4.13062 ∆v10 7 0.000161

11 7 v07 10.05562 ∆v07 0.000069

1 8 v18 9.16944 ∆v18 0.000089 2 8 v28 -2.25900 ∆v28 0.000162

3 8 v38 2.52309 ∆v38 0.000000

4 8 v48 4.11652 ∆v48 0.000117

5 8 v58 -4.47209 ∆v58 - 0.000062

6 8 v68 -5.60264 ∆v68 - 0.000066

7 8 v78 0.87461 ∆v78 - 0.000003 8 8 v88 -0.64263 ∆v88 0.000042

9 8 v98 0.48178 ∆v98 - 0.000005 10 8 v10 8 -2.11583 ∆v10 8 - 0.000089 11 8 v08 -2.70574 ∆v08 - 0.000078

1 9 v19 -1.14149 ∆v19 - 0.000006 2 9 v29 0.34255 ∆v29 0.000104

3 9 v39 -0.84035 ∆v39 0.000105

4 9 v49 2.09797 ∆v49 0.000068 5 9 v59 -1.55615 ∆v59 - 0.000213 6 9 v69 1.53547 ∆v69 0.000004

7 9 v79 -3.22638 ∆v79 - 0.000121 8 9 v89 1.58313 ∆v89 0.000008

9 9 v99 -3.74085 ∆v99 - 0.000116 10 9 v10 9 7.59907 ∆v10 9 0.000120 11 9 v09 1.64954 ∆v09 0.000095 1 10 v110 3.98237 ∆v110 0.000086

2 10 v210 0.70985 ∆v210 0.000000 3 10 v310 -2.22015 ∆v310 - 0.000105

4 10 v410 -1.73414 ∆v410 - 0.000050 5 10 v510 -0.65714 ∆v510 - 0.000053

6 10 v610 3.46032 ∆v610 - 0.000002 7 10 v710 0.52589 ∆v710 0.000001

8 10 v810 0.66482 ∆v810 - 0.000037

9 10 v910 -0.79967 ∆v910 0.000051 10 10 v10 10 -0.84294 ∆v10 10 0.000024

11 10 v010 1.56299 ∆v010 0.000023

147

TABLE A6.2 The Weights and Adjustment Weight from Hidden to Output Layers for Model 1

Layer Weight (w) Weight Delta (∆w)

Hidden Output Symbol Value Symbol Value

1 1 w11 8.22012 ∆w11 0.000173

2 1 w21 8.05748 ∆w21 - 0.000035 3 1 w31 -11.81557 ∆w31 - 0.000167

4 1 w41 -3.95643 ∆w41 - 0.000045

5 1 w51 5.81931 ∆w51 0.000052

6 1 W61 7.02754 ∆w61 - 0.000014 7 1 W71 -8.57063 ∆w71 0.000022

8 1 W81 -9.37805 ∆w81 0.000009

9 1 W91 7.71279 ∆w91 0.000144

10 1 W101 -4.73709 ∆w101 - 0.000066

11 1 w01 0.26200 ∆w01 - 0.000031

148




1 1 v11 -0.0334 ∆v11 0.00011

2 1 v21 11.1398 ∆v21 0.00015 3 1 v31 -5.6442 ∆v31 -0.00012

4 1 v41 -4.0072 ∆v41 -0.00019

5 1 v51 7.0809 ∆v51 0.00018

6 1 v01 -0.1922 ∆v01 -0.00004 1 2 v12 8.2227 ∆v12 -0.00015

2 2 v22 3.4059 ∆v22 0.00006

3 2 v32 -0.1190 ∆v32 0.00005

4 2 v42 -10.1258 ∆v42 -0.00007

5 2 v52 16.2961 ∆v52 0.00012

6 2 v02 1.2471 ∆v02 0.00006 1 3 v13 0.7145 ∆v13 -0.00004

2 3 v23 3.9817 ∆v23 0.00008 3 3 v33 -0.7252 ∆v33 -0.00013

4 3 v43 -8.0775 ∆v43 -0.00012

5 3 v53 3.4977 ∆v53 0.00036

6 3 v03 2.8402 ∆v03 -0.00012

1 4 v14 -1.8782 ∆v14 -0.00016 2 4 v24 5.2004 ∆v24 -0.00007

3 4 v34 -3.2810 ∆v34 -0.00001 4 4 v44 -0.3858 ∆v44 0.00006

5 4 v54 5.0386 ∆v54 0.00027 6 4 v04 1.1258 ∆v04 -0.00004

1 5 v15 -24.6426 ∆v15 -0.00045

2 5 v25 7.6951 ∆v25 0.00004 3 5 v35 -15.0260 ∆v35 -0.00028 4 5 v45 -7.1960 ∆v45 -0.00009

5 5 v55 12.4219 ∆v55 0.00033

6 5 v05 15.7090 ∆v05 0.00020 1 6 v16 0.6352 ∆v16 0.00000

2 6 v26 1.9198 ∆v26 0.00002 3 6 v36 -1.0198 ∆v36 -0.00002

4 6 v46 0.4504 ∆v46 0.00001

5 6 v56 0.2992 ∆v56 0.00002

6 6 v06 0.7232 ∆v06 0.00002

149

TABLE A6.3 The Weights and Adjustment Weight from Input to Hidden Layers for

Model 2 (continued)



1 7 v17 -0.7268 ∆v17 0.00001

2 7 v27 4.0865 ∆v27 0.00023

3 7 v37 -2.5184 ∆v37 -0.00003 4 7 v47 0.5646 ∆v47 -0.00001

5 7 v57 0.0617 ∆v57 0.00001

6 7 v07 1.0739 ∆v07 -0.00004 1 8 v18 -1.0847 ∆v18 -0.00015

2 8 v28 0.9248 ∆v28 -0.00015 3 8 v38 -2.5657 ∆v38 -0.00016 4 8 v48 0.9038 ∆v48 -0.00002

5 8 v58 0.5833 ∆v58 0.00001

6 8 v08 -0.0928 ∆v08 -0.00005 1 9 v19 0.4316 ∆v19 0.00000

2 9 v29 1.3301 ∆v29 0.00000 3 9 v39 -0.3654 ∆v39 0.00000 4 9 v49 0.2382 ∆v49 0.00000

5 9 v59 0.0814 ∆v59 0.00001 6 9 v09 0.0871 ∆v09 0.00001

1 10 v110 8.2650 ∆v110 0.00026

2 10 v210 -10.8962 ∆v210 0.00020 3 10 v310 7.6095 ∆v310 0.00005 4 10 v410 6.1864 ∆v410 -0.00004

5 10 v510 2.5224 ∆v510 -0.00034 6 10 v010 -10.7778 ∆v010 0.00004

150

TABLE A6.4 The Weights and Adjustment Weight from Hidden to Output Layers for Model 2

Layer Weight (w) Weight Delta (∆w)

Hidden Output Symbol Value Symbol Value

1 1 w11 -7.9749 ∆w11 -0.00011

2 1 w21 -11.5476 ∆w21 0.00002 3 1 w31 5.9794 ∆w31 0.00002

4 1 w41 -4.2249 ∆w41 -0.00013

5 1 w51 7.1779 ∆w51 0.00010

6 1 W61 1.1031 ∆w61 0.00004 7 1 W71 4.0469 ∆w71 0.00015

8 1 W81 2.6501 ∆w81 0.00011

9 1 W91 0.1909 ∆w91 0.00000

10 1 W101 9.3942 ∆w101 0.00016

11 1 w01 3.2862 ∆w01 0.00004

151

CHAPTER 7

EARLY WARNING SYSTEM MODELS:

COMPARISON AND CONSISTENCY

7.1. Introduction

In the preceding chapters three models have been developed in an effort to find

a suitable early warning system (EWS) model for predicting Indonesian

currency crises, namely the signal model in Chapter 4; discrete choice, that is,

probit and logit models in Chapter 5; and the artificial neural network (ANN)

model in Chapter 6. In those chapters, the performance of each model was

evaluated using six assessment methods.1 Based on these assessment methods,

the models generally performed well in predicting the crises. However, because

a comparison across the models has not been attempted, this study has not

determined which is the best of these three EWS models for predicting currency

crises in Indonesia.

This chapter will, therefore, attempt to achieve three objectives: first, to evaluate

and compare the performance of these three EWS models in order to define the

best EWS model in predicting Indonesian currency crises using the 24-month

crisis window for both in-sample and out-of-sample; second, to analyse the

sensitivity and consistency of these models when the underlying assumption of

crisis window or prediction horizons is shortened from 24 months to 6, 12 and

18 months; third, to compare the prediction results of these models for these

shorter crisis windows. This approach will allow an assessment of the

consistency and sensitivity across the three models and will thus allow for a

comparison to ascertain if the results obtained using the benchmark crisis

window are still valid at these shorter crisis windows. In addition, it also adds

1 Please see Chapter 4 for a more detailed explanation of the assessment methods used to evaluate the performance of the proposed EWS models in predicting Indonesian currency crises.

152

to the previous studies in this field where the performance of predictive models

across different prediction horizons have never been compared.

The discussion in this chapter will be organised as follows. Section 7.2 compares

the prediction results of the models for the 24-month crisis window. Section 7.3

conducts the sensitivity analysis for these models across the shorter crisis

windows. Section 7.4 compares the prediction results of these models across the

shorter crisis windows. Section 7.5 presents the concluding remarks.

7.2. Modelling Results Using a 24-month Crisis Window

Although these models have their advantages and disadvantages over each

other, this section of the study evaluates and compares the performance of these

models in order to determine the best model for predicting the currency crises

in Indonesia. Towards that goal, this study first compares their in-sample

predictions, followed by an evaluation of their out-of-sample predictions.

7.2.1. In-Sample Predictions Using a 24-month Crisis Window

This section evaluates the performance of three EWS models by comparing the

in-sample predictions. Figure 7.1 presents their in-sample probability of a crisis

from 1970/1971 to 1995. In this figure, the currency crises will be determined

whenever the probability of crises shifts across the cut-off probability (Pr*) or

threshold. For this purpose, four cut-off probabilities are set, namely 20%, 30%,

40% and 50%. In addition, Table 7.1 presents a comparison of the in-sample

assessment results for each model based on these thresholds.

Based on Figure 7.1, these models are generally able to predict the three in-

sample currency crises, with their respective probability of a crisis increasing

during the 24 months prior to these currency crises. However, in comparing the

results, this figure indicates that the ANN model was superior to the other two

models, namely the signal and probit models, as it was able to capture the

entire period of 24 months prior to these crises.

FIGURE 7.1

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FIGURE 7.1 In-Sample Prediction Using a 24-month Crisis Window

(a) The signal model

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154

This result is also supported by the percentage of pre-crisis periods correctly

called reported in Table 7.1. This assessment method indicates that the ANN

model is able to predict 99% at Pr*=50% and 100% at Pr*=20%. On the other

hand, the probit and signal models were only able to predict 75% and 57% at

Pr*=50% and 94% and 83% at Pr*=20%, respectively.

In addition, Figure 7.1 also indicates that the signal model has less ability to

capture the tranquil periods because it sends more false alarms than other

models. This is because its warning signals are not followed by any currency

crises within 24 months. The finding is also supported by the percentage of false

alarms sent by these models, with the signal model sending more false alarms

than the probit and ANN models for all Pr*. According to Table 7.1, the signal

model is able to predict the tranquil periods at about 74% for the cut-off

probability or Pr*=20%. While the probit and ANN models are able to capture

more than 87% and 100%, respectively. As Pr* increases to 50%, the ability of

these models also increases but the signal model still has less ability than the

other two models (being able to capture 92% (signal model), 94% (probit model)

and 100% (ANN model), respectively).

Furthermore, we assess the overall performance of the model against all

observations including the pre-crisis and tranquil periods for the entire in-

sample periods, Table 7.1 verifies that the ANN model performed better than

the other models, as it was able to capture 98% at Pr*=20% and almost 100% at

Pr*=50%, while the probit and signal models captured 89% and 77% at Pr*=20%,

and 89% and 83% at Pr*=50%, respectively. Finally, the scores of QPS and GSB

in Table 7.1 also indicate that these models generally perform well in predicting

the in-sample currency crises in Indonesia, for their score is close to zero, which

represents perfectly accurate prediction and calibration. However, as with the

other assessment methods, these methods also indicate that the ANN model is

superior when compared to the other models, as its scores are the lowest

recorded.

155

TABLE 7.1 In-sample Evaluation Using a 24-month Crisis Window

Pr* Assessment methods In-sample* (1970/71-1995)

Signal Probit ANN

20%

% of observations correctly called 76.53% 88.67% 98.33%




QPS 0.4695 0.2267 0.0333

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QPS 0.3344 0.2133 0.0067

GSB 0.0041 0.0004 0.0000 * The performance assessment for signal is based on January 1970 to December 1995, while the two

models, namely probit and ANN models are based on January 1971 to December 1995

7.2.2. Out-of-Sample Prediction Using a 24-month Crisis Window

This section evaluates the performance of the three EWS models that have

been developed in previous chapters by comparing their out-of-sample

prediction results. For this purpose, Figure 7.2 shows the probability of a

crisis for each model during the period January 1996 to September 2008 in

addition to a comparison of the assessment results based on four cut-off

probabilities as shown in Table 7.2.

FIGURE 7.2 Out-of-Sample Prediction

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As previously noted, the currency crisis that occurred in this period was

only one, namely, the Asian Financial Crisis in 1997/98; therefore, all these

EWS models will be evaluated based on their ability to predict this crisis.

Figure 7.2 shows that in general these three models were capable of doing

so, for their probability of a crisis increased within the period of a crisis.

However, their ability to capture the entire pre-crisis episode, which is

indicated by the yellow shaded area in Figure 7.2, varied greatly among

them.

Table 7.2 also shows that all models were able to predict this crisis.

Nevertheless, the ANN model performed better than the other two models,

as it was able to predict 90% at Pr*=50% to 97% at Pr*=20% of the pre-crisis

periods. While the signal and probit models could only predict 73% and

53% of the pre-crisis periods at Pr*=20%, when the cut-off probability

increased to 50%, the predictive capabilities of both models fell to 30% and

43%, respectively.

With regard to the timing of warning signals transmitted, Figure 7.2 shows

that the ANN model sent warning signals of the Asian Financial Crisis from

January 1996, with the probability of Indonesia having this crisis within 24

months being about 66%. The signal model was also able to predict the

existence of this crisis from April 1996, with probability of a crisis being

36%. Unlike the other two models, the probit model started to send warning

signals late in January 1997, with the probability of a crisis of about 66%.

In addition, although there was no currency crisis occurring after the Asian

Financial Crisis of 1997/98, Figure 7.2 clearly shows that the three models

were still sending lots of warning signals. These can be interpreted as false

alarms because their probability of a crisis passed their cut-off probabilities,

but no currency crisis occurred within 24 months after these signals were

received. Based on Table 7.2, at Pr*=20% the probit model sent more false

alarms than the signal and ANN models. However when Pr* increased to

50%, the signal model sent slightly more warnings than the probit model,

158

that is, 77% vs. 76%, while the ANN model had the lowest percentage of

false alarms, 56%.

TABLE 7.2 Out-of-Sample Evaluation Using a 24-month Crisis Window

Pr* Assessment methods

Out-of-sample

1996-1998 1996-2008

Signal Probit ANN Signal Probit ANN

20%


% of pre-crisis periods correctly called 73.33% 53.33% 96.67% 73.33% 53.33% 96.67%



QPS 0.4444 1.1111 0.2222 1.1373 1.0327 0.6144

GSB 0.0988 0.0988 0.0062 0.4307 0.2222 0.1730

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QPS 0.4444 1.2222 0.2778 1.1373 0.9412 0.5621

GSB 0.0988 0.1543 0.0015 0.4307 0.1367 0.1300

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QPS 1.1667 1.2222 0.2778 0.6667 0.8758 0.5098

GSB 0.6806 0.1543 0.0015 0.0069 0.1047 0.1047

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QPS 1.1667 1.2778 0.3333 0.6667 0.7843 0.4837

GSB 0.6806 0.1867 0.0000 0.0069 0.0578 0.0821

These false alarms also decreased the ability of the models to capture the

tranquil period during this period, compared to the in-sample period. For

example, the signal model was only able to capture 36% at Pr*=20% to 77%

at Pr*=50%, the probit model could only predict 47% at Pr*=20% to 65% at

Pr*=50%, while the ANN model was able to capture around 63% at Pr*=20%

to 72% at Pr*=50%. Generally, these three EWS models have proved

themselves in terms of their accuracy and calibration, as indicated by their

QPS and GSB scores being close to zero.

Finally, to conclude this section, it was found that these three EWS models

were able to predict the Indonesian currency crises for both in-sample and

159

out-of-sample periods as shown in Figures 7.1 and 7.2. This predictive

ability was also indicated by the percentage of pre-crisis correctly called in

Tables 7.1 and 7.2. However the ANN model performed better than the

other two models, as it was able to capture 100% of the in-sample pre-crisis

periods and 90% at Pr*=50% to 97% at Pr*=20% for the out-of-sample pre-

crisis periods. This positive picture is also supported by other assessment

methods covered in Tables 7.1 and 7.2.

The next section will explore the sensitivity of these three EWS models by

assessing their consistency in predicting Indonesian currency crises with

shorter crisis windows, namely 6, 12 and 18 months.

7.3. The Sensitivity Tests for Shorter Crisis Windows

Like previous studies by Kaminsky and Reinhart (1999), Goldstein et al. (2000),

Zhuang and Dowling (2002), Edison (2000) and so on, this study utilises three

EWS models to predict the Indonesian currency crises using a 24-month crisis

window. However, following Kaminsky (1999) and Goldstein et al. (2000),

which did the sensitivity test for their signal model using shorter crisis

windows of 12 and 18 months, this study analyses the sensitivity and

consistency of these three models in predicting currency crises within shorter

crisis windows of 6, 12, and 18 month durations.

Although these models predict the Indonesian currency crises using these

shorter crisis windows, for consistency and comparison, the same data set will

be used for both in-samples and out-of-samples. For example, except for the

signal model which uses the in-sample data from 1970 to 1995, other models,

namely the probit and ANN models, use the in-sample data from 1971 to 1995,

while the same out-of-sample data set from 1996 to 2008 is used. To assess the

sensitivity and consistency of these models, an attempt is made to modify and

re-estimate the signal model, followed by similar treatment for the probit and

ANN models.

160

7.3.1 The Signal model

This subsection analyses the sensitivity and consistency of the signal model in

predicting the currency crises in Indonesia using three crisis windows. In re-

estimating this signal model, two options related to determine the noise-to-

signal ratio (NSR) for each leading indicators are adopted. NSR plays a vital

role here in choosing the set of leading indicators to be used and to assign

weights to each indicator to form the composite index. In the first option, this

study applies the benchmark signal model that was developed in Chapter 4 and

uses the same noise-to-signal ratio (NSR) for all leading indicators with the

benchmark model. This model adjusts its crisis window with new shorter crisis

windows to accommodate these changes. This model can be described as the

signal model with a fixed NSR. In contrast, for the second option, similar

procedures are adopted with the benchmark model to recalculate the lowest

NSR for each leading indicator based on their ability to predict the crisis within

these new crisis windows and is later called the signal model with adjusted

NSR.

For this purpose, this study re-estimates the signal model that was discussed in

Chapter 4 for various shorter crisis windows by developing three new signal

models dedicated for each crisis window. For example, signal 1 is the signal

model for predicting the currency crises using a 6-month crisis window, signal

2 is the signal model for predicting the currency crises using a 12- month crisis

window, while signal 3 is the signal model dedicated to predict the currency

crises using a 18-month crisis window. However, to distinguish between these

two options of signal models, the symbols “a” that refer to option 1 and “b” for

option 2, are used.

Following an evaluation of the performance of these models in predicting the

Indonesian currency crises for each crisis window, the performance will be

compared with the benchmark signal model. This will be done by using the 24-

month crisis window to assess the sensitivity and consistency of this approach

when the assumption of crisis windows changes.

161

The Signal Model with Fixed NSR

As a consequence of using the same NSR with the benchmark model, this

model uses the same number and list of variables as the benchmark model

developed in Chapter 4. Furthermore, the composite index held by each new

model is also the same as the benchmark model. However, the difference

between the benchmark model and these new models lies in calculating the

probability of a crisis using Equation 4.3. As mentioned previously, this study

found that there are three in-sample currency crises in Indonesia from 1970 to

1995 when using Equation 3.2. Therefore, when calculating the probability of a

crisis using Equation 4.3, these three new signal models consider a fewer

number of months in the period of crisis compared to the benchmark model

which leads to differences in the probability of a crisis for each model. For

example, as the benchmark model uses the 24-month crisis window, the

number of months in its crisis period is 72 months. On the other hand, signal 1a,

which predicts a crisis within the 6-month crisis window, only has 18 months,

and signal 2a, which uses a 12-month crisis window, has 36 months. Signal 3a,

using the 18-month crisis window, has only 54 months.

Figure 7.3 shows the probability of a crisis for these three new models in

predicting the in-sample currency crises, while Figure 7.4 presents their

probability of a crisis in predicting the out-of-sample currency crisis. In

addition, the in-sample performance assessment for these models is presented

in Table 7.3, while Table 7.4 presents the out-of-sample performance assessment

for these models.

The Signal Model with Fixed NSR: In-Sample Prediction

Figure 7.3 reveals that these signal models do not perform very well, being only

able to predict two of the three in-sample currency crises, with their probability

of a crisis only increasing during the first two pre-currency crisis periods. In the

last in-sample the crisis in September 1986 could not be predicted using these

models, their probability of a crisis remaining low during this pre-crisis period.

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FIGURE 7.3 The Signal Model with Fixed NSR’s In-Sample Prediction

(a) A 6-month crisis window (signal 1a)

(b) A 12-month crisis window (signal 2a)

(c) A 18-month crisis window (signal 3a)

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For example, they show the probability of a crisis on average during this pre-

crisis period is not significant, as shown by the figures of 7% (signal 1a), 10%

(signal 2a) and 17% (signal 3a). Figure 7.3 also shows that compared to the other

crisis windows, the ability of the signal model in predicting a crisis within the 6-

months crisis window (signal 1a) is more limited, its maximum probability of a

crisis being only 32%. Thus if the cut-off probability (Pr*) increases to 40% or

more, this model cannot send any warning signals because its probability of a

crisis is lower than this threshold. It is also indicated by the percentage of the

pre-crisis period accurately predicted by these models in Table 7.3.

TABLE 7.3 The Signal Model with Fixed NSR’s In-Sample Evaluation

Pr* Assessment methods In-sample (1970-1995)

6m 12m 18m 24m

20%

% of observations correctly called 90.68% 91.96% 85.21% 76.53%

% of pre-crisis periods correctly called 55.56% 58.33% 64.81% 82.90%

% of tranquil periods correctly called 92.83% 96.36% 89.49% 74.47%

% of false alarms of total alarms 67.74% 32.26% 43.55% 48.78%

QPS 0.1865 0.1608 0.2958 0.4695

GSB 0.0035 0.0005 0.0013 0.0457

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GSB 0.0035 0.0005 0.0013 0.0457

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% of false alarms of total alarms NA 32.26% 19.35% 30.65%

QPS 0.1158 0.1608 0.2251 0.3344

GSB 0.0067 0.0005 0.0109 0.0041

50%




% of false alarms of total alarms NA 32.26% 19.35% 30.65%

QPS 0.1158 0.1608 0.2251 0.3344

GSB 0.0067 0.0005 0.0109 0.0041

Note: Pr*: the cut-off probability

Based on Table 7.3, at Pr*=20%, these models can only predict about 56% (for

signal 1a), 58% (for signal 2a) and 65% (signal 3a). However, when Pr* increases,

except for signal 2a, the ability of the other models in predicting crises falls. For

example, when Pr*=50%, the capacity of signal 3a drops to 46%, and signal 1a

164

cannot capture any currency crises at all. Likewise, the benchmark model is able

to predict crises by 83% at Pr*=20%, but when Pr* increases to 50%, its ability

declines to 57%, or is slightly lower than signal 2a.

In contrast, the ability of these models in capturing tranquil periods increases in

line with the increase in Pr*. For example, the ability of these models in

capturing the tranquil periods tends to increase with the increase in the cut-off-

probability. When Pr*=20%, these models are able to capture the tranquil

periods around 90%, thus much higher than the benchmark model.

Furthermore, when Pr* increases to 50%, the ability of these models also

increase to 100% (signal 1a), 96% (signal 2a) and 98% (signal 3). Again, these

figures are much higher than that of the benchmark model. Similar results also

apply when predicting all observations, in addition to the level of accuracy and

calibration of these models, which are represented by low scores of QPS and

GSB recorded in Table 7.3. Conversely, the percentage of false alarms relative to

total alarms from these models tends to fall in line with the increasing ability of

the model in capturing the tranquil period and the cut-off probability.

The Signal Model with Fixed NSR: Out-of-Sample Prediction

Figure 7.4 presents the ability of these models to predict the out-of-sample

crises from 1996 to 2008, while the performance assessment of these models is

shown in Table 7.4. Based on Figure 7.4, these models are generally able to

capture the Asian Financial Crisis, as their probability of a crisis increases

within their crisis windows, as indicated by the yellow shaded area. However

their performance is limited, for they are unable to capture the entire pre-crisis

periods.

165

FIGURE 7.4 The Signal Model with Fixed NSR’s Out-of-Sample prediction

(a) A 6-month crisis window (signal 1a)

(b) A 12-month crisis window (signal 2a)

(c) A 18-month crisis window (signal 3a)

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166

The finding is also supported by Table 7.4, which indicates that signal 3a is able

to predict 50% of its pre-crisis periods, while the other two models fail to

perform as well because they are only able to cover 19% (signal 1a) and 23%

(signal 2a) of pre-crisis periods at Pr*=20%. However, when Pr* increases to

50%, except for signal 2a, the ability of these models to capture their pre-crisis

widows declines. For example, the pre-crisis prediction results for signal 3a

drop to 18%, while signal 1a is unable to send any warning signals, as its

probability of a crisis is less than the cut-off probability.

The results show that the performance of the benchmark model is still better

than those models with shorter crisis windows. In other words, for the signal

model, using shorter crisis windows tends to degrade the ability of this model

to predict currency crises in Indonesia. Moreover, Figure 7.4 shows that these

models also send several false alarms, the numbers tending to increase along

with the longer prediction horizon or crisis window. In Table 7.4, during crisis

periods, except for signal 1a, no model sends out false alarms. However, for the

entire out-of-samples, at Pr*=20%, signal 2a sends less false alarms than the

other models, which send more than 50%. Furthermore when Pr* is increased,

the number of their false alarms declined. For example, at Pr*=50%, except for

signal 1a, only 29% of signals transmitted by signals 2a and 3a could be

categorized as false alarms. This table indicates that these models perform well

compared to the benchmark model. The number of false alarms also determines

the ability of the model to capture the tranquil periods. As these models send

fewer false signals than the benchmark model, so the ability of these models in

capturing the tranquil period is also stronger than the benchmark model.

16

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168

It is also evident that the number of observations can be predicted accurately by

these models, thus performing better than the benchmark model. Likewise, the

accuracy and calibration are good, as the QPS and GSB scores are low. This

result is not surprising because the tranquil period is much longer than the

period of crisis, so although the benchmark model is able to better predict the

pre-crisis period, it is less able to capture the tranquil periods because its

probability of a crisis is more volatile than that of the signal models with their

shorter crisis windows.

The Signal Model with Adjusted NSR

As previously mentioned, in this second scenario, the same steps in developing

the signal model as used in Chapter 4 are applied. Similar to the benchmark

model, this study uses 55 leading indicators and transforms them into dummy

warning signals or sit=1 whenever they pass their thresholds, otherwise there

are no signals, or sit=0. Following Table 4.1 in Chapter 4, these warning signals

can be classified into four groups based on their ability to predict the currency

crises within specific crisis windows. Based on this classification of warning

signals, the lowest noise-to-signal ratio (NSR) is calculated for each leading

indicator. This is done using Equation 4.1 by adjusting the threshold and

ranking these leading indicators based on the lowest NSR. This represents the

best indicator to predict the currency crisis within specific crisis windows. The

results of which are presented in Table 7.5.

Similar to the benchmark approach in Chapter 4, this study also selects the set

of leading indicators based on their NSR being less than unity, or NSR<1. Due

to using different crisis windows, Table 7.5 shows that the number of leading

indicators used for constructing a composite index and the rank of best leading

indicators based on the lowest score of NSR, also changed. For example, unlike

the benchmark model in Chapter 4 that used 39 of 55 leading indicators, the

signal model for the 6 months crisis window (or signal 1b) used 40 of 55 leading

indicators, while both signal models using 12 months (or signal 2b) and 18

months (or signal 3b) crisis windows used 37 of 55 leading indicators.

169

TABLE 7.5 List Indicator Based on NSR for Various Crisis Windows

No LEADING INDICATORS 6 m 12 m 18 m 24 m

NSR R NSR R NSR R NSR R

1 Real US$/yen exchange rate1 0.25 11 0.02 2 0.04 3 0.03 1

2 Short-term capital flows to GDP 0.08 3 0.01 1 0.02 1 0.03 2

3 Current account balance to GDP 0.11 4 0.1 5 0.02 2 0.04 3

4 US annual growth rate 0.4 21 0.11 6 0.04 4 0.06 4

5 US real interest rate3 0.8 38 1.14 41 1.69 42 0.09 5

6 Short -term capital flows to GDP3 0.19 7 0.05 3 0.08 5 0.12 6

7 US real interest rate 0.44 25 0.41 21 0.25 13 0.13 7

8 Loans to deposits3 0.58 30 1.15 42 0.41 21 0.23 8

9 M1 to GDP3 1.75 42 0.27 17 0.15 7 0.23 9

10 Real effective exchange rate1 0.12 6 0.07 4 0.11 6 0.24 10

11 Domestic real interest rate3 NA 50 NA 50 NA 50 0.26 11

12 Exports2 0.26 14 0.24 13 0.22 11 0.29 12

13 M1 to GDP 0.55 29 0.13 7 0.21 8 0.32 13

14 Government consumption to GDP 0.23 9 0.35 20 0.3 16 0.33 14

15 Foreign reserves in months of imports

0.06 1 0.14 8 0.22 9 0.34 15

16 Trade balance to GDP3 0.3 16 0.41 22 0.4 20 0.34 16

17 Foreign reserves2 0.19 8 0.15 9 0.22 10 0.34 17

18 Foreign reserves in months of imports3

0.32 18 0.21 11 0.38 19 0.36 18

19 Government consumption to GDP3 0.9 40 0.58 30 0.42 24 0.38 19

20 Domestic real interest rate differential from US rate3

NA 47 NA 47 NA 47 0.38 20

21 Lending to deposit rate spread 0.25 12 0.24 14 0.24 12 0.39 21

22 Current account balance to GDP3 0.39 20 0.5 24 0.74 34 0.4 22

23 Deposits to M23 0.26 13 0.55 29 0.48 25 0.4 23

24 Net credit to government to GDP3 0.07 2 0.15 10 0.25 14 0.41 24

25 Fiscal balance to GDP3 0.32 17 0.29 18 0.26 15 0.43 25

26 Deposits in BIS banks to reserves3 NA 46 NA 46 NA 46 0.44 26

27 Real exchange rate against US$1 0.68 34 0.58 31 0.53 27 0.52 27

28 Domestic real interest rate 0.41 22 0.35 19 0.35 17 0.57 28

29 M2 to reserves3 0.28 15 0.23 12 0.37 18 0.58 29

30 Fiscal balance to GDP 0.25 10 0.26 15 0.42 23 0.6 30

31 M2 multiplier2 0.5 27 0.52 25 0.69 33 0.62 31

32 M2 multiplier 0.65 31 0.54 27 0.57 28 0.62 32

33 Oil price 0.78 37 0.73 35 0.64 31 0.62 33

34 M2 to reserves 0.12 5 0.26 16 0.42 22 0.65 34

35 Domestic credit to GDP3 0.78 35 0.68 34 0.65 32 0.73 35

36 Central bank credit to the public sector to GDP

0.65 32 0.63 33 0.64 30 0.74 36

37 Domestic real interest rate differential from US rate

0.44 24 0.52 26 0.5 26 0.82 37

38 Real commercial bank deposits2 0.42 23 0.44 23 0.62 29 0.95 38

39 Trade balance to GDP 0.37 19 0.6 32 0.8 35 0.99 39

40 Short -term external debt to reserves3

0.51 28 0.84 36 0.98 37 1 40

170

TABLE 7.5 List Indicators Based on NSR for Various Crisis Windows (Continued)

No LEADING INDICATORS 6 m 12 m 18 m 24 m

NSR R NSR R NSR R NSR R

41 Foreign liabilities to foreign assets3 0.78 36 1.11 39 1.29 40 1.3 41

42 Domestic credit to GDP 1.02 41 1.11 40 1.2 38 1.35 42

43 Net credit to government to GDP 0.67 33 1.07 38 1.25 39 1.64 43

44 Central bank credit to the public sector to GDP3

0.48 26 0.87 37 1.41 41 1.67 44

45 Short-term external debt to reserves NA 43 NA 43 NA 43 1.7 45

46 Imports2 0.9 39 0.55 28 0.88 36 1.84 46

47 Stock price index in local currency2 NA 53 NA 53 NA 53 1.95 47

48 Foreign liabilities to foreign assets NA 44 NA 44 NA 44 3.96 48

49 Loans to deposits NA 48 NA 48 NA 48 6.14 49

50 Deposits in BIS banks to reserves NA 45 NA 45 NA 45 NA 50

51 Deposits to M2 NA 49 NA 49 NA 49 NA 51

52 Lending ]deposit rate spread3 NA 51 NA 51 NA 51 NA 52

53 Oil price2 NA 52 NA 52 NA 52 NA 53

54 Industrial/manufacturing production index2

NA 54 NA 54 NA 54 NA 54

55 Domestic consumer price index2 NA 55 NA 55 NA 55 NA 55

# of leading indicator used in the model 40 37 37 39

# of leading indicator excluded in the model

15 18 18 16

Spearman’s rank correlation coefficient relative to the benchmark model

0.608 0.709 0.791

Note: 1 deviation from trend-HP filter; 2 12m% change; 312m change; NA: not available, R: Rank of indicators based on the lowest NSR

In addition, depending on the rank of the best leading indicators used for each

crisis window, and using the Spearman rank correlation coefficient2, this study

finds that there is significant positive correlation between the rank of the

leading indicator and the crisis windows, because the score of the Spearman

rank correlation increases in line with an increase in the crisis window. For

example, the Spearman correlation rank for the 6 months crisis window is 0.6;

however, when the crisis window is extended into 12 and 18 months, the

Spearman rank correlation also increases to 0.7 and 0.8.

Furthermore, the composite index using the selected leading indicators is

generated. However, as the ability of each indicator to predict the crisis varies

the benchmark approach is adopted. Following Equation 4.2, the inverse of

NSR is employed as the weight of the signals of these selected leading

2 Spearman’s rank correlation coefficient can be used to identify the strength of correlation between two variables, and see whether the correlation is positive or negative. This coefficient ranges from -1 to 1 and value of -1 means perfectly negative correlation, if its value is zero it represents no correlation and if the value is 1 it means perfectly positive correlation.

171

indicators. As indicators with a low NSR refer to better indicators in predicting

a crisis, they contribute more in developing the composite index than indicators

with higher NSR. Finally, using Equation 4.3, this composite index can be

transformed into the model’s probability of a crisis. The models’ probabilities of

a crisis for each crisis window are presented in Figures 7.5 and 7.6. These cover

(a) Signal model with adjusted NSR for 6-month crisis windows, or signal 1b,

(b) Signal model with adjusted NSR for 12-month crisis window, or signal 2b,

and (c) Signal model with adjusted NSR for 18-month crisis window, or signal

3b. In addition, the performance assessments of these models for all crisis

windows are presented in Tables 7.6 and 7.7.

The Signal Model with Adjusted NSR: In-Sample Prediction

Figure 7.5 indicates that these signal models are able to predict three in-sample

currency crises within three shortened crisis windows as their probability of a

crisis increases during the pre-crisis periods. It is also supported by the

percentage of pre-crisis periods recorded in Table 7.6 where these three models

can capture 78% (signal 1b), 83% (signal 2b) and 80% (signal 3b) at Pr*=20%.

However, the ability of signal 1b in predicting these in-sample crises within the

6-month crisis windows is more limited that of other models. Even though, the

probability of a crisis of signal 1b increases during all the pre-crisis periods, its

maximum probability of a crisis is 45%. Thus, if Pr* then increases to 50%, signal

1b fails to send any warning signals.

In contrast, even though Pr* increases to 50%, the other models can still capture

the pre-crisis periods by 58% (signal 2b) and 46% (signal 3b). In comparison

with the benchmark model developed in Chapter 4, which is able to capture

pre-crisis periods by 83% at Pr*=20% but decreases to 57% when Pr* increases to

50%, with the exception of signal 1b, it was found that this model is not so

sensitive to the change in crisis windows.

FIGURE 7.5 The Signal M

(a) A 6-month crisis window

(b) A 12-month crisis window

(c) A 18-month crisis window

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172

Model with Adjusted NSR’s In-Sample Prediction

month crisis window (Signal 1b)



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cc6m signal_6m

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cc18m signal_18m

ample Prediction

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173

This is because it is consistently able to predict the pre-crisis period within

shortened crisis windows for all cut-off probabilities, while signal 2b performs

better than the benchmark model. Moreover, Figure 7.5 indicates that the ability

of these models to predict the third in-sample crisis is also limited, as the

models’ probability of a crisis for this crisis across the crisis windows ranges

from 30% (signal 2b) to 45% (signal 1b), so when Pr* increases to 50%, these

models cannot send any warning alarms for this crisis episode.

In terms of false signals, and based on Figure 7.5, this study indicates that these

models also sent more false alarms, particularly at the beginning of the 1970s

and late 1980s to early 1990s, though these are not significant. It is also

supported by the percentage of false alarms in Table 7.6, which indicates all

these models sent false alarms around 50% at Pr*=20%. However, when Pr* was

increased to 50%, the number of false alarms decreased to 30% (signal 2b), and

19% (signal 3b), while for predicting a crisis within a 6-month crisis window,

signal 1b sent no false alarms because its maximum probability of a crisis was

45%, or less than the cut-off probability of 50% (Pr*=50%).

In capturing the tranquil period, this study also found that the prediction

results of these models for shorter crisis windows also performed well and were

an improvement over the benchmark model. It is also supported by the ability

to capture all observations including the pre-crisis periods and tranquil periods.

Furthermore, Table 7.6 also indicates that these models perform well in

predicting the pre-in-sample currency crisis periods for these crisis windows, as

shown by low QPS and GSB scores, which are close to zero, thus indicating

perfect accuracy and calibration. In addition, they are also better than the

benchmark model, as their scores are slightly lower.

174

TABLE 7.6 The Signal Model with Adjusted NSR’s In-Sample Evaluation


6m 12m 18m 24m

20%





QPS 0.1350 0.2444 0.3859 0.4695

GSB 0.0035 0.0140 0.0299 0.0457

30%





QPS 0.1350 0.1608 0.2830 0.4695

GSB 0.0035 0.0005 0.0013 0.0457

40%





QPS 0.1350 0.1608 0.2251 0.3344

GSB 0.0035 0.0005 0.0109 0.0041

50%





QPS 0.1158 0.1608 0.2251 0.3344

GSB 0.0067 0.0005 0.0109 0.0041


The Signal Model with Adjusted NSR: Out-of-Sample Prediction

Figure 7.6 presents the results of the out-of-sample predictions for the signal

model for all crisis windows and the performance evaluation results across the

crisis windows. The cut-off probabilities are presented in Table 7.7. Similar to

the in-sample predictions, in Figure 7.6, the models are able to predict the out-

of-sample crises at all prediction horizons, as their probability of a crisis

increased during the pre-crisis periods.

FIGURE 7.6 The

0%

20%

40%

60%

80%

100%

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175

The Signal Model with Adjusted NSR’s Out-of

(a) A 6-month crisis window (signal 1b)

(b) A 12-month crisis window (signal 2b)

(c) A 18-month crisis window (signal 3b)

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cc18m signal_18m

of-Sample Prediction

(signal 2b)

(signal 3b)

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176

It was also demonstrated by the percentage of pre-crisis periods correctly called

in Table 7.7. Based on this method, these models can capture 50% (signal 1b),

64% (signal 2b) and 57% (signal 3b) at Pr*=20%. As with the in-sample

predictions, the ability of signal 1b is more limited than that of other models

because its maximum probability of a crisis is only 45%, so when Pr* increased

to 50% this model was unable to capture any crises. Furthermore, for other

models, if Pr* increases to 50%, they are still able to capture this pre-crisis

period, but unlike the benchmark model, which is able to predict 30% of pre-

crisis periods, the prediction results decrease and are insignificant, as they can

only capture 18% (signal 2b) and 11% (signal 3b). For more details, see Table

7.7.

In terms of the timing of warning signals sent by these models, with the

exception of signal 1b, signal 2b sends warning signals quite late. After

December 1996 its probability of a crisis was not significant, being at 29%. On

the other hand, signal 3b warned of the presence of crisis earlier in 1996,

although its probability of a crisis remained low at 16.1%. Moreover signal 2b

sent significant warning signals with the probability of Indonesia having a crisis

being 68% within 12 months after December 1997. Similarly, in predicting the

18-month pre-crisis period, signal 3b sent warning signals after December 1996

with the probability of a crisis being 36%. Following this, it fluctuated around

16% to 36%, before its probability of a crisis increased to 81% in January 1998

where it remained for 3 months. It then dropped to 36% in April 1998 and

recorded a further drop to 16% in June 1998. This occurred one month after the

resignation of Soeharto as the second President of the Republic of Indonesia.

Furthermore, if Pr*=50%, signal 1b failed to send any warning signals but the

other models, even though too late, still warned the government of the Republic

of Indonesia of the presence of the Asian Financial Crisis in December 1997

(signal 2b), or January 1998 (signal 3b). In contrast, the benchmark model was

able to send warning signals reaching 84% in January 1997.

With respect to the number of false alarms sent by these models during the

crisis period from 1996 to 1998, unlike the benchmark model, which sent no

177

false alarms because it predicted the whole of the tranquil periods during this

period, these models with shorter crisis windows sent false alarms about 11%

(signal 1b), 30% (signal 2b) and 6% (signal 3b) at Pr*=20%. In line with the

increase in Pr*, the models’ percentage of false alarms also decreased, as for

example at Pr* =50%, where all models sent no false alarms. It can also be seen

in the ability of these models to capture the tranquil periods during the period

1996 to 1998 when there was an increase in line with increase in Pr*. For

example, when Pr*=50%, these models captured 100% of the tranquil periods.

For the entire out-of-sample period from 1996 to 2008, as there was no currency

crisis defined after the Asian Financial Crisis in 1997/98, all warning signals

after this can be classified as false alarms. As shown in Figure 7.6, these models

sent many false alarms during this period compared to the in-sample

predictions from 1970 to 1995 shown in Figure 7.5. This finding is supported in

Table 7.7 which shows that at Pr*=20% these models sent more false alarms,

being 60% (signal 1b), 79% (signal 2b) and 80% (signal 3b). However, as Pr*

increased, the percentage of false alarms to total signals in the models declined.

For example, when Pr*=50%, unlike signal 1b, which sent no false alarms, the

other models sent them at 33% (signal 2b) and 84% (signal 3b).

Consequently, these false alarms also determine the ability of models to capture

the tranquil periods. For example, at Pr*=20%, these models were able to

capture 91% (signal 1b), 59% (signal 2b) and 49% (signal 3b). As Pr* increased,

the ability of models to predict tranquil periods also increased, for example at

Pr*=50%, signal 1b was able to capture 100% of the tranquil periods, while

signal 2b and signal 3b were able to capture 98% and 87% respectively. This

proved better than the benchmark model, which was able to capture 76%.

Similar results are also derived using the percentage of observations correctly

called, as well as in term of accuracy and calibration indicated by the low QPS

and GSB scores. For more details see Table 7.7.

17

8

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179

As the main objective of the EWS model is to assess the consistency of this

model in being able to predict the episodes of currency crises rather than the

tranquil periods, focus is directed towards the ability of the model to predict the

currency crisis events within specific crisis windows. Furthermore, by applying

this model to predict crises within various shorter crisis windows, this study

can assess the sensitivity and consistency of this model, in addition to finding

the best prediction horizon for predicting Indonesian currency crises.

As a consequence, this study found that for the in-sample prediction, the signal

model was less sensitive to the change of crisis windows, as the model

consistently performed well in predicting the in-sample currency crises. In

addition, signal 2, when predicting the currency crisis within a 12-month crisis

window, performed better than the benchmark model. In contrast, for the out-

of-sample prediction, this study found that the signal model was more sensitive

and less consistent, for the models with shorter crisis windows performed less

well when compared to the benchmark model.

Finally, to conclude this subsection, it was found that the application of the

signal model using a 24-month crisis window was more consistent when

predicting the Indonesian currency crises, for it could predict the out-of-sample

crisis better than the model using shorter crisis windows. This finding also

supports previous studies such as Kaminsky et al. (1998), Edison (2000),

Goldstein et al. (2000), and Kaminsky (1999), all of which indicate a 24-month

crisis window is more consistent when predicting currency crises. Moreover, in

comparing the performance between these two signal model options, this study

found the signal model based on option 2 generally performed better than the

model based on option 1 across crisis windows and cut-off probabilities.

7.3.2. The Probit Model

For consistency and comparability, and similar to the previous subsection,

sensitivity tests are here carried out so as to observe the consistency of the

probit model by predicting the Indonesian currency crises within shorter crisis

windows, such as 6, 12, and 18 months. After adjusting the dependent variable

for these three shorter crisis windows, as in Chapter 5, the probit model applies

180

Huber/White robust errors and covariance to predict the currency crises in

Indonesia within the 24-month crisis window. This is later referred to as the

benchmark model. Towards this purpose, these new crises dependent variables

are regressed using the same 10 explanatory variables with the benchmark

model. The regression results for these three shorter crisis windows are shown

in Table 7.8.

TABLE 7.8 The Probit Model’s Regression Results for Various Crisis Windows

Variables Exp. sign

Regression Coefficient

6m 12m 18m 24m

Constant -5.686*

(-2.812) -5.465*

(-2.880) -7.830*

(-4.005) -3.806**

(-2.175)

Real US$/yen exchange ratea - 0.047*

(3.176) 0.053*

(4.137) 0.032*** (1.799)

-0.002 (-0.175)

Short-term capital flows to GDP

- -63.660* (-3.180)

-81.496* (-3.916)

-85.959* (-3.576)

-91.104* (-3.964)

US annual growth rate - 0.014

(0.180) 0.114

(1.516) 0.137***

(1.798) 0.097

(1.175)

US real interest ratec + -0.243** (-2.133)

-0.411* (-3.656)

-0.510* (-3.027)

-0.061 (-1.264)

US real interest rate + 0.213** (2.415)

0.226** (2.191)

0.185* (3.313)

0.065 (1.231)

Loans to depositsc + -2.157** (-2.043)

-2.430** (-2.112)

-0.731 (-1.320)

1.405* (3.238)

M1 to GDPc + -60.915* (-3.321)

-7.345 (-0.378)

35.000** (1.929)

31.081*** (1.828)

Real effective exchange ratea + 0.018** (2.334)

0.036* (4.737)

0.049* (4.983)

0.049* (4.323)

Exportsb - -0.028*

(-2.963) -0.022*

(-3.351) -0.020*

(-2.962) -0.017*

(-2.962)

M1 to GDP + 31.736 (1.541)

29.517 (1.495)

58.253* (2.799)

28.212 (1.495)

McFadden R2 0.401 0.493 0.584 0.597

Number of observation 300 300 300 300 Note: the z-statistics are shown in parentheses; One star (*) indicates statistical significance at a 1% level, Two stars (**) indicates significance at a 5% level. Three stars (***) indicates significance at a 10% level; Alphabet (a) indicates 12 m change; Alphabet (b) indicates 12 m % change, Alphabet (c) indicates deviation from trend-HP Filter

As a consequence of using these shorter crisis windows, Table 7.8 shows that

similar to the benchmark model, these new probit models also have some

explanatory variables with statistically insignificant coefficients and wrong

signs, and the number of variables varied across crisis windows. For examples,

probit 1 has two insignificant variables, while probit 2 and 3 have three, and

one insignificant variable, respectively. Unlike the benchmark model that has

181

only two independent variables with the wrong sign, probit 1 and 2 have five

variables with wrong signs, while probit 3 has four such variables.

Moreover, to find out the contribution of each explanatory variable in

determining the currency crises in Indonesia, this study also calculates the

marginal effect for each explanatory variable for all models relative to the

currency crisis dependent variables. These results are presented in Table 7.9. As

with the benchmark model, this study found that all models indicated the

short-term capital flows to GDP as the most significant contributors in

determining the Indonesian currency crises for all crisis windows.

TABLE 7.9 Determinants of Indonesian Currency Crises

Variables dProb/dx

6m 12m 18m 24m

Real US$/yen exchange ratea 0.0002 0.0009 0.0011 -0.0002

Short-term capital flows to GDP -0.3088 -1.4183 -3.0354 -10.2901

US annual growth rate 0.0001 0.0020 0.0048 0.0109

US real interest ratec -0.0012 -0.0072 -0.0180 -0.0068

US real interest rate 0.0010 0.0039 0.0065 0.0073

Loans to depositsc -0.0105 -0.0423 -0.0258 0.1587

M1 to GDPc -0.2955 -0.1278 1.2359 3.5106

Real effective exchange ratea 0.0001 0.0006 0.0017 0.0055

Exportsb -0.0001 -0.0004 -0.0007 -0.0020

M1 to GDP 0.1539 0.5137 2.0571 3.1866

The probability of a crisis for probit models with shorter crisis windows is

presented in Figures 7.7 and 7.8. This shows (a) the probit model for predicting

a 6-month crisis window (probit 1), (b) the probit model for predicting a 12-

month crisis window (probit 2), and (c) the probit model for predicting a 18-

month crisis window (probit 3). In addition, the performance assessments for

these models are presented in Tables 7.10 for in-sample evaluation, and 7.11 for

out-of-sample evaluation.

182

The Probit Model: In-Sample Prediction

As indicated in Figure 7.7, the ability of probit models to capture all three in-

sample currency crises, namely November 1978, April 1983 and September

1986, tended to increase in line with the increase in the prediction horizon or

crisis window. Compared to the other two models, probit 1 has the lowest

predictive ability, as it cannot predict the third in-sample crisis. This is also

supported by the ratio of pre-crisis period accurately predicted in Table 7.10.

For example, at Pr*=20%, these models predict the in-sample currency crises

quite well, as their prediction reached around 61% (probit 1), 81% (probit 2) and

87% (probit 3). Furthermore their predictive power drops following a further

increase in Pr*. For example, at Pr*=50%, probit 1 is only able to capture 28% of

pre-crisis periods, while the other two models still perform well as they predict

58% (probit 2) and 65% (probit 3). However, during this period, the benchmark

model performed better than these models, as it was able to predict these pre-

crises periods by 75% (Pr*=50%) to 94% (Pr*=20%). Moreover, Table 7.10 also

shows that the in-sample predictive ability of the probit model tends to increase

as the prediction horizon is extended.

This figure also indicates that these models send false alarms during which

their probability of a crisis crosses their thresholds beyond their pre-crisis

period, as indicated by the yellow shaded areas. Regarding the number of false

alarms relative to the total signals, Table 7.10 confirms that these models send

more false alarms compared to the benchmark model. For example, the number

of in-sample false alarms relative to total signals was around 60% (probit 1),

51% (probit 2) and 41% (probit 3) at Pr*=20%, decreasing to around 20% as Pr*

increased to 50%.

Regarding the ability to predict the entire observation, including pre-crisis and

tranquil periods, these models show positive performance, as they are able to

predict more than 87% at Pr*=20%, and show a further increase to around 96%

when Pr*=50%.

FIGURE 7.7

0%

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183

FIGURE 7.7 The Probit Model’s In-Sample Prediction

(a) A 6-month crisis window (probit 1)

(b) A 12-month crisis window (probit 2)

(c) A 18-month crisis window (probit 3)

19

74

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cc6m probit_6m

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cc12m probit_12m

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cc18m probit_18m

ample Prediction

(probit 1)

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184

These results are supported by high performance in capturing the tranquil

periods and the tranquil period is much longer than the crisis periods.

Similarly, these models also have high accuracy and calibration as indicated by

lower QPS and GSB scores. Furthermore, their results are slightly better than

the benchmark model.

TABLE 7.10 The Probit Model’s In-Sample Evaluation


6m 12m 18m 24m

20%





QPS 0.1533 0.2467 0.2600 0.2267

GSB 0.0018 0.0118 0.0139 0.0150

30%





QPS 0.1267 0.2067 0.1867 0.2000

GSB 0.0000 0.0006 0.0032 0.0044

40%





QPS 0.1000 0.1667 0.1867 0.1733

GSB 0.0011 0.0006 0.0001 0.0014

50%





QPS 0.1000 0.1400 0.1867 0.2133

GSB 0.0027 0.0018 0.0022 0.0004 Note: Pr*: the cut-off probability

The Probit Model: Out-of-Sample Prediction

To test the ability of these models in predicting crisis, this study predicts the

out-of-sample currency crisis from 1996 to 2008. Based on Figure 7.8, these

models are able to predict the presence of the Asian Financial Crisis, as their

probability increases over their cut-off probabilities during this pre-crisis

period, which is indicated by the yellow shaded area. It is also seen in Table

7.11 where these three models are able to predict this crisis by 38% (probit 1),

185

59% (probit 2) and 57% (probit 3) at Pr*=20%. Except probit 1, the performances

of other probit models are better than the benchmark model. However as Pr*

increases to 50%, the prediction of probit 1 remains the same, although the

performance of the other models, including the benchmark model, decreases,

but probit 3 is still able to predict the crisis by 46%, which is better than the

benchmark model that is only able to predict by 43%.

In terms of the timing of warning signals, and based on figure 7.8, probits 1 and

2 sent warning signals much later than probit 3. Probit 1 was expected to send

warning signals from March 1997 but it only started to send significant

warnings from January 1998 when the probability of a crisis reached 100%.

Similar to the benchmark model, probit 2 also sent warning signals from

January 1997 with the probability of a crisis being 34%. This was four months

after its pre-crisis period, which started in September 1996. Unlike these two

models, probit 3 was able to transmit its warning signals from the beginning of

its pre-crisis period, although its probability of a crisis was not so significant,

being only 18%. This increased to 34% in June 1996, but then continued to fall,

to then jump to 75% after January 1997.

As with the benchmark model, Figure 7.8 also shows lots of false alarms with

signals failing to be followed by any crisis within their crisis windows.

Furthermore, the number of false alarms generated in this period exceeded

those in the in-sample period. Based on Table 7.9, these three models sent out

many false alarms, showing that for the entire out-of-sample, for example at

Pr*=20%, the average false alarms relative to all signals was about 80%. Even

when Pr* was raised to 50%, the ratio of false alarms remained high.

Although the ability of these models in capturing the tranquil periods is still

better than the benchmark model, these high false signals impede the ability of

the models in capturing the information. It also eventually affects the ability of

these models to capture the entire observations, for both periods of crisis and of

tranquility. For the entire samples, they can predict about 38% (probit 1) of all

observations, 59% for probit 2 and 57% for probit 3, at Pr*=20%.

FIGURE 7.8 The P

(a) A 6-month crisis window

(b) A 12-month

(c) A 18-month crisis window (prob

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186

The Probit Model’s Out-of-Sample Prediction

month crisis window (probit 1)



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187

However, when Pr* increases, their prediction for all observations only slightly

increases, although this is still better than the benchmark model. Similar results

have also occurred when related to the level of accuracy and calibration of these

models, which are based on the QPS and GSB scores. This indicates that these

models are more useful than the benchmark models, as seen in Table 7.11.

The above discussion shows that in comparing the predicted results within

these various alternative crisis windows, this model was less effective when

predicting currency crises within the 6-months crisis window. Although probit

1 was capable of predicting the out-of-sample crisis by 38%, its generated

signals proved too late for January 1998. But the ability of this model tends to

increase when used for longer prediction horizons or crisis windows, such as 12

and 18 months, for which the model was consistently able to predict the

currency crises both in-sample and out-of-sample, as well as to show improved

accuracy in the timing of signals transmitted. Furthermore, Figure 7.8 and Table

7.11 also show that the performance of probit 3 was slightly better than the

benchmark model in predicting the Asian Financial Crisis in 1997/98, as it was

in the timing of warning signals sent.

7.3.3. The Artificial Neural Network Model

As with the two previous models, namely the signal and probit models, this

study also conducts a sensitivity test to see the consistency of the artificial

neural network (ANN) model. This was applied in Chapter 6 to predict the

Indonesian currency crises when the crisis windows shortened to 6 (ANN 1), 12

(ANN 2), and 18 (ANN 3) months. For this purpose, and similar to the

benchmark ANN model, this study applies the multilayer feed-forward neural

network with three layers, which consists of ten input neurons, one hidden

layer with ten hidden neurons and one output neuron. In addition, to increase

the performance of these new ANN models, they were trained by employing

the back-propagation supervised learning algorithm using the in-sample data

set from January 1971 to December 1995. These models were then simulated

using the same training parameters to the benchmark model, as presented in

Table 7.12.

18

8

TA

BL

E 7

.11

The

Pro

bit

Mod

el’s

Ou

t-of

-sam

ple

Eva

luat

ion

Pr*

Assessment methods


1996-1998

1996-2008

6m

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QP

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QP

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67

1.16

67

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22

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37

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44

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55

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64

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60

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% o

f p

re-c

risi

s p

erio

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corr

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d

37.5

0%

27.2

7%

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3%

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3%

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0%

27.2

7%

46.4

3%

43.3

3%

% o

f tr

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70

.00%

57

.14%

25

.00%

0.

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80

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76

.34%

68

.80%

65

.04%

% o

f fa

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s 50

.00%

50

.00%

31

.58%

31

.58%

81

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83

.78%

75

.00%

76

.79%

QP

S

0.88

89

1.22

22

1.16

67

1.27

78

0.48

37

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44

0.70

59

0.78

43

GS

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50

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67

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92

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921

0.05

78

189

As in Chapter 6, and similar to the benchmark model, this model initially set its

connection weights randomly, and then during the training process, the

connection weights were adjusted gradually until the model’s output came

close to its target value, or the error becomes smaller, or the maximum number

of iterations was reached. The maximum number of iterations was set at 30000.

Based on these training parameters, after the model reached the maximum

number of iterations, it was found that the training error for these new ANN

models was much larger than the benchmark model (0.062), and varied across

crisis windows. For example, 0.5007 (ANN 1), 0.3847 (ANN 2) and 0.7203 (ANN

3).

TABLE 7.12 The Training Parameter for ANN Models

No Description Training Information

1 Type of network Multi-layer perceptron

2 Number of layers 3

3 Number of hidden layer 1

4 Number of input neurons 10

5 Number of hidden neurons 10

6 Number of output neurons 1

7 Activation functions Logistic

8 Performance function Mean squared error

9 Training algorithm back-propagation

10 Starting weights and biases Random

11 Number of iterations 30000

12 Training error 0.062

13 Learning rate (α) 0.010

14 Momentum factor (β) 0.800

Similar to the parametric approach using the probit model, and based on the

results of the training undertaken, this model can be used to show the average

contribution for each input’s neurons to the output neuron, that is, the

probability of the occurrence of currency crises in Indonesia. However, unlike

the parametric model, it should be noted that in the ANN model, beyond the

190

input neurons there are still many other factors that may affect or determine the

output of this model, such as the number of hidden layers and hidden neurons,

the value of momentum and learning rates, and the number of iteration.

Table 7.13 presents the average contribution of input neurons to the output

neuron for all crisis windows. Except for ANN 2 that refers to the US real

interest rate as the main contributor to its output, the other models, namely

ANN 1 and 3, as well as the benchmark model, point out that the real effective

exchange rate is the main contributor that determines the probability of the

occurrence of currency crises in Indonesia, the average contribution being 16-

17%. The table also shows that they vary across the prediction horizons for

other contributors.

The in-sample forecasting results of these models are presented in Figure 7.9,

while Figure 7.10 displays the out-of-sample forecasting results. Furthermore,

the in-sample performance assessment results are recorded in Table 7.14, while

Table 7.15 presents the out-of-sample assessment results.

TABLE 7.13 Average Contribution of Input Nodes to Output Node

No Description Average Contribution

6m 12m 18m 24m

1 Short-term capital flows to GDP 5.61% 6.90% 6.69% 8.64%

2 Exportsb 6.08% 5.19% 13.80% 8.59%

3 Real effective exchange ratea 17.02% 8.79% 15.87% 17.40%

4 M1 to GDP 8.08% 6.06% 5.20% 6.93%

5 M1 to GDPc 10.72% 10.25% 5.15% 7.28%

6 Loans to depositsc 9.55% 12.11% 9.30% 13.11%

7 US real interest rate 14.10% 16.57% 15.27% 10.55%

8 US real interest ratec 9.25% 11.06% 12.38% 10.84%

9 US annual growth rate 9.76% 14.95% 7.60% 6.94%

10 Real US$/yen exchange ratea 9.81% 8.12% 8.73% 9.72% Notes: a deviation from trend-HP filter, b 12 months percentage change, c 12 months change

191

The ANN Model: In-sample Prediction

Figure 7.9 shows that similar to the benchmark model, these models can also

predict all in-sample currency crises accurately, as their probability of a crisis

rises throughout the entire pre-crisis periods, these being marked as yellow

areas. It is also supported by the percentage of the pre-crises period correctly

called in Table 7.14 that indicates all models are able to predict 100% of pre-

crises periods for all cut-off probabilities.

In addition, as with the benchmark model, this figure shows these models send

fewer false alarms, thus increasing the accuracy of these models in capturing

the tranquil periods. It also increases the ability of these models to capture the

whole observation for both crisis and tranquil periods. Furthermore, this table

records that their QPS and GSB scores are almost close to zero, which indicates

that the accuracy and calibration of these models are almost perfect.

FIGURE 7.9 The A

(a) A 6-month crisis window (ANN 1)

(b) A 12-month crisis window (ANN 2)

(c) A 18-month crisis windo

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192

The ANN Model’s In-Sample Prediction

month crisis window (ANN 1)



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TABLE 7.14 The ANN Model’s In-Sample Evaluation


6m 12m 18m 24m

20%





QPS 0.0267 0.0133 0.0400 0.0333

GSB 0.0004 0.0001 0.0008 0.0006

30%





QPS 0.0133 0.0000 0.0133 0.0067

GSB 0.0001 0.0000 0.0000 0.0000

40%





QPS 0.0067 0.0000 0.0067 0.0067

GSB 0.0000 0.0000 0.0000 0.0000

50%





QPS 0.0067 0.0000 0.0067 0.0067

GSB 0.0000 0.0000 0.0000 0.0000


The ANN Model: Out-of-sample Prediction

In this section, an attempt was made to test the ability of these models to predict

the Asian Financial Crisis that occurred in Indonesia during the period 1997/98.

The prediction of these models can be seen in Figure 7.10, and the results of the

assessment of the performance in predicting the out-of-sample are presented in

Table 7.15.

FIGURE 7.10 The A

(a) A 6-month

(b) A 12-month crisis window (ANN 2

(c) A 18-month crisis window (ANN 3)

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The ANN Model’s Out-of-Sample Prediction




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195

Based on this figure, it is found that these models can predict the existence of

this crisis where the crisis probability of these three models is developed to

predict crisis for each crisis window, which tends to rise prior to the crisis. Even

though the predictions are not as good as the benchmark ANN model, Table

7.11 shows that these models are capable of predicting the pre-crisis period by

about 73%, except for ANN 1 which can only predict about 56% at Pr*=20%. As

with other previous models, the ability tended to fall as the cut-off probability

increases. At Pr*=50%, the prediction ability of ANN 3 fell by 14% from 75% to

61%, while the other two models only decreased slightly to 50% (ANN 1) and

68% (ANN 2).

With regard to the timing of the signals transmitted, this figure shows that these

three models were capable of sending warning signals from early 1996, or a few

months before their prediction horizon. This was unlike the benchmark model

which used the 24-crisis window, but because of using shorter crisis windows,

these signals can be classified as false alarms because no currency crises

occurred within the crisis windows (Goldstein et al., 2000). However, based on

this figure, ANN 1 sent warning signals from June 1997 with the probability of

a crisis of 64%, and even after that it dropped to 0% but rose again to 100% in

January 1998. Meanwhile for ANN 2, although a bit late, this model was

capable of sending warning signals from January 1997, with the probability of a

crisis reaching 100%, but dropping to 12% in November 1997 before rising again

to 100% in January 1998.

Unlike the other two ANN models with shorter crisis windows, ANN 3 was

able to transmit warning signals from the beginning of the prediction horizon

with its probability of a crisis reaching 79%, despite the probability of a crisis

having come down in October 1996. This rose again to 68% in January 1997, but

after September 1997 it dropped dramatically before rising again to 100% in

January 1998. Furthermore, based on Figure 7.9, this study found that as its

prediction horizon expanded the model tended to transmit its warning signals

earlier.

196

Apart from the ability to predict crises, Figure 7.10 also shows that after the

Asian Financial Crisis in 1997/98, and until the end of 2008, these models still

sent many warning signals of impending crisis in Indonesia. However, as

previously stated, based on Equation 3.2 in Chapter 3, no currency crises were

found in Indonesia during this period and these signals can be classified as false

alarms. According to Table 7.15, during this period, compared to the

benchmark model, these models sent more false alarms by 70% at Pr*=20%,

dropping slightly as Pr* increased to 50%. Furthermore these high false alarms

also lowered the ability of the model in capturing the tranquil periods. This

table also indicates that the ability of the models in capturing the tranquil

period tended to decline with the increase in the prediction horizons. It also

applies for the ability of the models to capture the whole observation, as well as

for the level of accuracy and calibration for these models.

To conclude, the purpose of this section has been to exercise the consistency of

the ANN model by applying the sensitivity test to assess its performance in

predicting currency crises in Indonesia for difference crisis windows. Based on

the discussion above, it is found that the ANN model consistently performed

very well in predicting the in-sample currency crises from January 1971 to

December 1995 for all crisis windows. With fewer false alarms it acted perfectly

in capturing tranquil periods and ultimately enhanced the level of accuracy and

calibration of this model for all crisis windows. For the out-of-sample

predictions from 1996 to 2008, although the prediction outcomes are not as

good compared to the in-sample prediction and benchmark models, this model

was still able to predict the Asian Financial Crisis by more than 50% to 75% of

its pre-crisis period.

19

7

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198

7.4. Models Comparison Using a Shorter Crisis Window

In the previous section, three EWS models were exercised to predict Indonesian

currency crises within three new crisis windows, which were shorter than crisis

window of the benchmark models. However, a comparison of their ability to

predict crisis for every crisis window was not done. Nevertheless, based on

these previous exercises, in predicting crisis within a 6-month crisis window,

this study found that with the exception of the ANN model, the prediction

results of the other two models, namely signal and probit models, were

inconsistent and more limited compared to the longer prediction horizons. As

the EWS model is forward looking, ideally warning signals that are sent must

provide sufficient time for policy makers to set up preventive measures. For

this, a 6-month crisis window is too short, particularly for government to act

upon these warning signals. Based on the exercise in the previous section,

except for the ANN model, other models tended to be late in sending warning

signals. For example, the signal model sent warning signals too early, but

because this crisis window was too short, the signals could be categorized as

false alarms.

For the 18-months crisis window, Goldstein et al. (2000) found that their

predictions were similar to the model with a 24-month crisis window. In the

empirical literature in this field, most studies have applied a 24-month crisis

window, while some of them also used a 12-month crisis window as an

alternative. However, they have rarely used the 6-month and 18-month crisis

windows.

Consequently, and following Bussiere and Fratzcher (2002), Kamin et al. (2007),

and Nag and Mitra (1999), in this section, a comparison will only be made for

the performance of these EWS models in predicting currency crises within the

12-months crisis windows. According to Bussiere and Fratzcher (2002), the 12-

months crisis window is an optimal combination that fits two opposing views,

the first of which indicates that economic variables perform poorly towards the

time of crisis, but on the other that policy makers need a fairly long time to be

able to take preventive measures. Consequently, this study also provides a

199

comparison of these models for the other two 6 and 18 months crisis windows,

as seen in the attachments.

As mentioned above, in this section, this study evaluates and compares the

performance of three EWS models based on their ability to predict the

Indonesian currency crises for both in-sample and out-of-sample periods. For

this purpose, Figure 7.11 presents the in-sample time-series probability of a

crisis, while the performance assessments are reported in Table 7.16. On the

other hand, the out-of-sample time-series probabilities of a crisis for these

models are displayed in Figure 7.12, and the performance assessments are

reported in Table 7.17.

7.4.1 In-Sample Prediction Using a 12-month Crisis Window

Based on Figure 7.11, it is found that these models can predict the in-sample

currency crises. However, unlike the ANN model, which is able to predict all

pre-crises periods, the other models even though being able to predict the first

two in-sample crises, have less predictive ability for the last in-sample crisis,

particularly the signal model. Table 7.16 indicates that at Pr*=20% these models

are capable of predicting the 12-months pre-crises periods quite highly at 83%

(signal), 81% (probit) and 100% (ANN). However, when Pr* is increased to 50%,

ANN is still able to predict these pre-crises periods by 100%, while the

prediction ability of the other two models decreases to 58%. In other words,

using Figure 7.11 and Table 7.16, it is found that the ANN model performs very

well in predicting the in-sample pre-crises and is also superior compared to the

signal and probit models.

FIGURE 7.11 In-Sample Prediction

(a)

(b) The p

(c)

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200

ample Prediction Using a 12-month Crisis Windows

(a) The Signal model


(c) The ANN model

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201

This figure also shows that these models send false alarms. This applies

particularly to the signal and probit models. Furthermore, the signal model sent

more false alarms (mainly in the 1970s), but the probit sent more at the

beginning of the 1990s. This in turn makes these models to perform less well in

the prediction of the tranquil periods compared to the ANN model. In addition,

the other performance assessment methods, such as the percentage of

observation correctly predicted, level of accuracy (QPS) and calibration (GSB),

also support these results and point out that ANN still performed better than

the signal and probit models.

TABLE 7.16 In-Sample Evaluation Using a 12-month Crisis Windows

Pr* Assessment methods In-sample (1970/71-1995)

Signal Probit ANN

20%





QPS 0.2444 0.2467 0.0133

GSB 0.0140 0.0118 0.0001

30%





QPS 0.1608 0.2067 0.0000

GSB 0.0005 0.0006 0.0000

40%





QPS 0.1608 0.1667 0.0000

GSB 0.0005 0.0006 0.0000

50%





QPS 0.1608 0.1400 0.0000

GSB 0.0005 0.0018 0.0000

202

7.4.2. Out-of-Sample Prediction Using a 12-month Crisis Window

This subsection evaluates and compares the performance of these models in

predicting the Asian Financial Crisis in 1997/98. For this purpose, Figure 7.12

presents the out-of-sample time-series probability of a crisis for these three

models from 1996 to 2008. To compare the performance of these models, this

study also uses six performance assessment methods and the results are

reported in Table 7.17.

Basically, these models were able to predict the presence of the Asian Financial

Crisis in Indonesia, as their probability of a crisis increased during the 12-

month pre-crisis period. To see this in more detail and the ability of these

models to predict this crisis and for ease of comparing their performance, this

study uses the percentage of pre-crisis periods correctly captured by the model

presented in Table 7.16. Based on this assessment, it was found that at Pr*=20%,

all models could predict well but that similar to the in-sample prediction, the

ANN model performed better when compared to the other two models. For

example 64% (signal), 59% (probit) and 73% (ANN). Unlike the other models,

when Pr* increased by 10% to 30%, the predictive ability of the signal dropped

dramatically to 18% and remained there even when Pr* was increased to 50%.

Meanwhile for probit and ANN models, their predictive ability decreased

gradually in line with the increase in Pr*, so that when Pr*=50%, the probit

model was only able to predict its pre-crisis period by 27%, while the ANN

model was able to predict by 68%. A prediction result that was much better

than the other models. For more details see Table 7.17.

Another important factor in the early warning system model is the timing of

warning signals. Ideally, this EWS model has to send warning signals before the

occurrence of a crisis, so that policy makers will still have sufficient time to take

preventive actions to overcome these threats.

FIGURE 7.12 Out

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Out-of-sample Prediction Using a 12-month



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204

Regarding the timing of warning signals transmitted by these models, in

Figure 7.12 these models would be expected to send warning signals from

the beginning of their 12-month crisis window, or September 1996, as

highlighted in yellow. However, in this figure, there are no models that were

able to send warning signals from the beginning of their crisis window in

September 1996. While the signal model sent warning signals after three

months, or after December 1996, its probability of a crisis of 29% was not too

significant. However it did send strong warning signals in December 1997

when its probability of a crisis increased to 68%.

Likewise, the probit model was only able to transmit its signal from January

1997, or one month later than the signal model, with the probability of a crisis of

34%. Despite some fluctuations, its probability of a crisis reached 47% in June

1997 before gradually falling. Furthermore, this model sent warning signals of a

crisis after January 1998, with the probability of Indonesia being hit by a crisis

within 12 months reaching 100%.

Regarding the time of the signals transmitted, Figure 7.12 shows that the ANN

model was capable of sending warning signals from early 1996, or a few

months before its prediction horizon, although these warning signals can be

categorized as false signals because no crisis occurred within its crisis window.

However, this model was unable to send its warning signals after the beginning

of its prediction horizon. Although a bit late, the ANN model was capable of

sending warning signals after January 1997 when the probability of a crisis

reached 100%. It dropped to 12% in November 1997 but then rose again to 100%

in January 1998.

20

5

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206

Despite these models being capable of predicting the Asian Financial Crisis, the

figure also shows they were still sending lots of warning signals even though

by definition this crisis was the only currency crisis in this period. Table 7.17

shows that during the period of crisis from 1996 to 1998, the ANN model sent

more false signals than the other models. For the entire out-of-sample period

from 1996 to 2008, at Pr*=20%, the percentage of false alarms transmitted by the

ANN model was 71%, or the smallest compared to the other models that sent

79% by signal and 77% by probit. However, when Pr* was increased to 50%, the

signal model sent fewer false signals than any other models.

Regarding false alarms, the figure indicates that the probability of a crisis of

these models generally increased after the Asian Financial Crisis until 2000. This

also occurred between mid-2001 and 2002. In addition, the probability of a crisis

of the signal and probit models also increased after the beginning of 2008. As

mentioned in previous chapters, unlike the parametric model (probit model),

one of the biggest criticisms of the non-parametric approach such as the ANN

and signal models is their inability to explain the causality between the

independent and dependent variables, thus making it difficult to explain the

fluctuation of their probability of a crisis.

As a consequence of sending fewer false alarms, the signal model provided

poorer performance than the other models when predicting the tranquil

periods. It also performed poorly in predicting the pre-crisis period, as the

number of months in the tranquil period was much larger than the number of

months in the crisis period. However, the model does perform well when the

whole observation, including crisis and tranquil periods, is used. Similar results

are also found in terms of accuracy and calibration for predicting the whole

observation.

However, because the main objective of the EWS model is predicting crisis

rather than tranquil periods, evaluating and choosing the optimal EWS models

is based on the ability to predict crises and the timing accuracy of warning

signals sent. It has been found that as with the benchmark models, the ANN

207

model performs better than the other models in predicting crises within the

shorter crisis window of 12 months.

7.5. Conclusions

In this chapter three things have been achieved, namely, the full comparison of

the three main models in predicting crises in the 24-month crisis window;

performing the sensitivity tests to see the consistency of the three models in

predicting crises within three shorter crisis windows; and comparing the ability

of these three EWS models for predicting crises within shorter crisis windows.

When comparing the performance of these three EWS models in predicting

crises within the 24-month crisis window, it was found that the performance of

the ANN model was better than signal and probit models for both within

samples from 1970/71 to 1995 and out-of-samples from 1996 to 2008. For

example, in predicting the three in-sample currency crises, these models were

able to predict about 99-100% (ANN model), 75-94% (probit model), and 57-

83% (signal model) for the 24-month pre-crisis period. Similarly for the out-of-

sample crisis - the Asian Financial Crisis - ANN still performed better than the

other models, as it was able to predict the 24-month pre-crisis periods at about

90-97%, while signal and probit models were only able to capture 30-73% and

43-53% of the pre-crisis periods, respectively.

Moreover, in terms of the timing of warning signals sent by these models for

predicting the Asian Financial Crisis in 1997/98, ANN was able to send

warning signals of the presence of this crisis from January 1996, with the

probability of Indonesia having this crisis within 24 months at about 66%, while

the signal model started to send its signals four month later, from April 1996,

with the probability of a crisis at 36%. In contrast, a year later, the probit model

started to warn about this crisis when the probability of a crisis reached 66% in

January 1997.

208

In evaluating the consistency and sensitivity of these models as a response to

the change in the prediction horizon or crisis window, this study applied three

new crisis windows that were shorter than the default or benchmark crisis

window of 24 months, namely 6, 12, and 18 months. As a result all models were

repeated to predict crises within these new crisis windows.

Two signal models were adopted based on two options related to the selection

of leading indicators. The first signal model used the same set of indicators as

the benchmark model, while the second model used the same procedure with

the benchmark model by recalculating the lowest noise-to-signal ratio for each

leading indicator for these three shorter crisis windows. This affected the set of

leading indicators for constructing the composite index that tended to vary

across crisis windows, including the benchmark model.

The results showed that the in-sample prediction capability of these models was

less sensitive to the change in crisis windows, because in general these models

consistently performed well even though they were unable to predict the third

in-sample crisis. In predicting the out-of-sample currency crisis, these models

were however more sensitive to the change in crisis windows, particularly

when using the 30% or more cut-off probability, as their prediction results were

insignificant compared to the benchmark model. A comparison between these

two signal models indicates that the second model generally performed better

than the first model across crisis windows and cut-off probabilities. In addition,

compared to the other crisis windows, their ability to predict crises within the 6-

month crisis window was limited because their maximum probability of a crisis

was 32% (signal 1a) and 45% (signal 1b). When the cut-off-probability increased

to 50%, these models failed to send any warning signals.

For the probit model, the results of the sensitivity tests showed that the

sensitivity and consistency varied depending on the prediction horizons, and

the longer the crisis window was used the more consistent this model was in

predicting crises relative to the benchmark model. This model’s ability to

predict pre-crisis periods tended to increase when the crisis window was

209

expanded and the out-of-sample predicted results for the 18-month crisis

window were even better than the benchmark model.

In contrast, the results of sensitivity tests for the ANN model show that it was

not sensitive to the changes in the crisis window as it was consistently able to

predict the pre-crisis period very well, particularly for its in-sample prediction.

Likewise, for the out-of-sample prediction, the test indicated that it was less

sensitive, but when compared with the in-sample prediction, the out-of-sample

prediction was more sensitive. Although their out-of-sample predictions are not

as good as the benchmark model, these models still consistently performed

well, for they were able to predict more than 50% of the pre-crisis periods for all

crisis windows and cut-off-probabilities of 50-56% (ANN 1), 68-75% (ANN 2)

and 61-75% (ANN 3).

In comparing the performance of these three EWS models in predicting crises

within the 12-month crisis windows, this study found that the results were in

accordance with those of the benchmark model and the ANN model still

performed better than the other two models. These results were also valid for

the comparison of other shorter crisis windows, namely the 6-month and 18-

month crisis windows for both within sample and out-of-sample, as well as all

four levels of cut-off-probabilities. For further details, a comparison of the

ability of these three models in predicting pre-crisis periods within various

prediction horizons for each boundary can be seen in Figures 7.13 and 7.14.

210

FIGURE 7.13 In-Sample Comparison Using Various Crisis Windows and Cut-off

Probabilities, 1970/71-1995

FIGURE 7.14 Out-of-sample Comparison Using Various Crisis Windows and Cut-

off Probabilities, 1996-2008

0%

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signal Probit ANN

Appendixes

FIGURE A7.1

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7.1 In-Sample Prediction Using a 6-month Crisis Window



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19

87

M0

7

19

88

M0

6

19

89

M0

5

19

90

M0

4

cc6m ann_6m

month Crisis Window

19

90

M0

4

19

91

M0

3

19

92

M0

2

19

93

M0

1

19

93

M1

2

19

94

M1

1

19

95

M1

0

19

90

M0

4

19

91

M0

3

19

92

M0

2

19

93

M0

1

19

93

M1

2

19

94

M1

1

19

95

M1

0

19

90

M0

4

19

91

M0

3

19

92

M0

2

19

93

M0

1

19

93

M1

2

19

94

M1

1

19

95

M1

0

212

TABLE A7.1 In-Sample Evaluation Using a 6-month Crisis Window


Signal Probit ANN

20%





QPS 0.1350 0.1533 0.0267

GSB 0.0035 0.0018 0.0004

30%





QPS 0.1350 0.1267 0.0133

GSB 0.0035 0.0000 0.0001

40%





QPS 0.1350 0.1000 0.0067

GSB 0.0035 0.0011 0.0000

50%





QPS 0.1158 0.1000 0.0067

GSB 0.0067 0.0027 0.0000

FIGURE A7.2 Out

0%

20%

40%

60%

80%

100%

19

96

M0

1

19

96

M0

7

19

97

M0

1

19

97

M0

7

19

98

M0

1

0%

20%

40%

60%

80%

100%

19

96

M0

1

19

96

M0

7

19

97

M0

1

19

97

M0

7

19

98

M0

1

0%

20%

40%

60%

80%

100%

19

96

M0

1

19

96

M0

7

19

97

M0

1

19

97

M0

7

19

98

M0

1

213

Out-of-Sample Prediction Using a 6-month Crisis Window



(c) The ANN model

19

98

M0

1

19

98

M0

7

19

99

M0

1

19

99

M0

7

20

00

M0

1

20

00

M0

7

20

01

M0

1

20

01

M0

7

20

02

M0

1

20

02

M0

7

20

03

M0

1

20

03

M0

7

20

04

M0

1

20

04

M0

7

20

05

M0

1

20

05

M0

7

cc6m signal_6m

19

98

M0

1

19

98

M0

7

19

99

M0

1

19

99

M0

7

20

00

M0

1

20

00

M0

7

20

01

M0

1

20

01

M0

7

20

02

M0

1

20

02

M0

7

20

03

M0

1

20

03

M0

7

20

04

M0

1

20

04

M0

7

20

05

M0

1

20

05

M0

7

cc6m probit_6m

19

98

M0

1

19

98

M0

7

19

99

M0

1

19

99

M0

7

20

00

M0

1

20

00

M0

7

20

01

M0

1

20

01

M0

7

20

02

M0

1

20

02

M0

7

20

03

M0

1

20

03

M0

7

20

04

M0

1

20

04

M0

7

20

05

M0

1

20

05

M0

7

cc6m ann_6m

month Crisis Window

20

06

M0

1

20

06

M0

7

20

07

M0

1

20

07

M0

7

20

08

M0

1

20

08

M0

7

20

06

M0

1

20

06

M0

7

20

07

M0

1

20

07

M0

7

20

08

M0

1

20

08

M0

7

20

06

M0

1

20

06

M0

7

20

07

M0

1

20

07

M0

7

20

08

M0

1

20

08

M0

7

21

4

TA

BL

E A

7.2

Ou

t-of

-Sam

ple

Ev

alu

atio

n U

sin

g a

6-m

onth

Cri

sis

Win

dow

Pr*

Assessment methods

Out-of-sample

1996-1998

1996-2008

Signal

Probit

ANN

Signal

Probit

ANN

20%

% o

f o

bse

rvat

ion

s co

rrec

tly

cal

led

75

.00%

55

.56%

47

.22%

86

.93%

74

.51%

73

.86%

%

of

pre

-cri

sis

per

iod

s co

rrec

tly

cal

led

50

.00%

37

.50%

56

.25%

50

.00%

37

.50%

56

.25%

% o

f tr

anq

uil

per

iod

s co

rrec

tly

cal

led

95

.00%

70

.00%

40

.00%

91

.24%

78

.83%

75

.91%

%

of

fals

e al

arm

s o

f to

tal

alar

ms

11.1

1%

50.0

0%

57.1

4%

60.0

0%

82.8

6%

78.5

7%

QP

S

0.50

00

0.88

89

1.05

56

0.26

14

0.50

98

0.52

29

GS

B

0.07

56

0.02

47

0.03

86

0.00

14

0.03

08

0.05

78

30%

% o

f o

bse

rvat

ion

s co

rrec

tly

cal

led

75

.00%

55

.56%

47

.22%

86

.93%

75

.82%

75

.16%

%

of

pre

-cri

sis

per

iod

s co

rrec

tly

cal

led

50

.00%

37

.50%

56

.25%

50

.00%

37

.50%

56

.25%

%

of

tran

qu

il p

erio

ds

corr

ectl

y c

alle

d

95.0

0%

70.0

0%

40.0

0%

91.2

4%

80.2

9%

77.3

7%

% o

f fa

lse

alar

ms

of

tota

l al

arm

s 11

.11%

50

.00%

57

.14%

60

.00%

81

.82%

77

.50%

Q

PS

0.

5000

0.

8889

1.

0556

0.

2614

0.

4837

0.

4967

G

SB

0.

0756

0.

0247

0.

0386

0.

0014

0.

0247

0.

0492

40

%

% o

f o

bse

rvat

ion

s co

rrec

tly

cal

led

75

.00%

55

.56%

44

.44%

86

.93%

75

.82%

74

.51%

%

of

pre

-cri

sis

per

iod

s co

rrec

tly

cal

led

50

.00%

37

.50%

50

.00%

50

.00%

37

.50%

50

.00%

%

of

tran

qu

il p

erio

ds

corr

ectl

y c

alle

d

95.0

0%

70.0

0%

40.0

0%

91.2

4%

80.2

9%

77.3

7%

% o

f fa

lse

alar

ms

of

tota

l al

arm

s 11

.11%

50

.00%

60

.00%

60

.00%

81

.82%

79

.49%

Q

PS

0.

5000

0.

8889

1.

1111

0.

2614

0.

4837

0.

5098

G

SB

0.

0756

0.

0247

0.

0247

0.

0014

0.

0247

0.

0452

50%

% o

f o

bse

rvat

ion

s co

rrec

tly

cal

led

55

.56%

55

.56%

44

.44%

89

.54%

75

.82%

74

.51%

%

of

pre

-cri

sis

per

iod

s co

rrec

tly

cal

led

0.

00%

37

.50%

50

.00%

0.

00%

37

.50%

50

.00%

%

of

tran

qu

il p

erio

ds

corr

ectl

y c

alle

d

100.

00%

70

.00%

40

.00%

10

0.00

%

80.2

9%

77.3

7%

% o

f fa

lse

alar

ms

of

tota

l al

arm

s 0.

00%

50

.00%

60

.00%

0.

00%

81

.82%

79

.49%

Q

PS

0.

8889

0.

8889

1.

1111

0.

2092

0.

4837

0.

5098

G

SB

0.

3951

0.

0247

0.

0247

0.

0219

0.

0247

0.

0452

FIGURE A7.3

0%

20%

40%

60%

80%

100%

19

71

M0

1

19

71

M1

2

19

72

M1

1

19

73

M1

0

19

74

M0

9

0%

20%

40%

60%

80%

100%

19

71

M0

1

19

71

M1

2

19

72

M1

1

19

73

M1

0

19

74

M0

9

0%

20%

40%

60%

80%

100%

19

71

M0

1

19

71

M1

2

19

72

M1

1

19

73

M1

0

19

74

M0

9

215

3 In-Sample Prediction Using a 18-month Crisis Window



(c) The ANN model

19

74

M0

9

19

75

M0

8

19

76

M0

7

19

77

M0

6

19

78

M0

5

19

79

M0

4

19

80

M0

3

19

81

M0

2

19

82

M0

1

19

82

M1

2

19

83

M1

1

19

84

M1

0

19

85

M0

9

19

86

M0

8

19

87

M0

7

19

88

M0

6

19

89

M0

5

19

90

M0

4

cc18m signal_18m

19

74

M0

9

19

75

M0

8

19

76

M0

7

19

77

M0

6

19

78

M0

5

19

79

M0

4

19

80

M0

3

19

81

M0

2

19

82

M0

1

19

82

M1

2

19

83

M1

1

19

84

M1

0

19

85

M0

9

19

86

M0

8

19

87

M0

7

19

88

M0

6

19

89

M0

5

19

90

M0

4

cc18m probit_18m

19

74

M0

9

19

75

M0

8

19

76

M0

7

19

77

M0

6

19

78

M0

5

19

79

M0

4

19

80

M0

3

19

81

M0

2

19

82

M0

1

19

82

M1

2

19

83

M1

1

19

84

M1

0

19

85

M0

9

19

86

M0

8

19

87

M0

7

19

88

M0

6

19

89

M0

5

19

90

M0

4

cc18m ann_18m

Crisis Window

19

90

M0

4

19

91

M0

3

19

92

M0

2

19

93

M0

1

19

93

M1

2

19

94

M1

1

19

95

M1

0

19

90

M0

4

19

91

M0

3

19

92

M0

2

19

93

M0

1

19

93

M1

2

19

94

M1

1

19

95

M1

0

19

90

M0

4

19

91

M0

3

19

92

M0

2

19

93

M0

1

19

93

M1

2

19

94

M1

1

19

95

M1

0

216

TABLE A7.3 In-Sample Evaluation Using a 18-month Crisis Window


Signal Probit ANN

20%





QPS 0.3859 0.2600 0.0400

GSB 0.0299 0.0139 0.0008

30%





QPS 0.2830 0.1867 0.0133

GSB 0.0013 0.0032 0.0000

40%





QPS 0.2251 0.1867 0.0067

GSB 0.0109 0.0001 0.0000

50%





QPS 0.2251 0.1867 0.0067

GSB 0.0109 0.0022 0.0000

FIGURE A7.4 Out

0%

20%

40%

60%

80%

100%

19

96

M0

1

19

96

M0

7

19

97

M0

1

19

97

M0

7

19

98

M0

1

0%

20%

40%

60%

80%

100%

19

96

M0

1

19

96

M0

7

19

97

M0

1

19

97

M0

7

19

98

M0

1

0%

20%

40%

60%

80%

100%

19

96

M0

1

19

96

M0

7

19

97

M0

1

19

97

M0

7

19

98

M0

1

217

Out-of-Sample Prediction Using a 18-month Crisis Window



(c) The ANN model

19

98

M0

1

19

98

M0

7

19

99

M0

1

19

99

M0

7

20

00

M0

1

20

00

M0

7

20

01

M0

1

20

01

M0

7

20

02

M0

1

20

02

M0

7

20

03

M0

1

20

03

M0

7

20

04

M0

1

20

04

M0

7

20

05

M0

1

20

05

M0

7

cc18m signal_18m

19

98

M0

1

19

98

M0

7

19

99

M0

1

19

99

M0

7

20

00

M0

1

20

00

M0

7

20

01

M0

1

20

01

M0

7

20

02

M0

1

20

02

M0

7

20

03

M0

1

20

03

M0

7

20

04

M0

1

20

04

M0

7

20

05

M0

1

20

05

M0

7

cc18m probit_18m

19

98

M0

1

19

98

M0

7

19

99

M0

1

19

99

M0

7

20

00

M0

1

20

00

M0

7

20

01

M0

1

20

01

M0

7

20

02

M0

1

20

02

M0

7

20

03

M0

1

20

03

M0

7

20

04

M0

1

20

04

M0

7

20

05

M0

1

20

05

M0

7

cc18m ann_18m

month Crisis Window

20

06

M0

1

20

06

M0

7

20

07

M0

1

20

07

M0

7

20

08

M0

1

20

08

M0

7

20

06

M0

1

20

06

M0

7

20

07

M0

1

20

07

M0

7

20

08

M0

1

20

08

M0

7

20

06

M0

1

20

06

M0

7

20

07

M0

1

20

07

M0

7

20

08

M0

1

20

08

M0

7

21

8

TA

BL

E A

7.4

Ou

t-of

-Sam

ple

Ev

alu

atio

n U

sin

g a

18

-mon

th C

risi

s W

ind

ow

Pr*

Assessment methods

Out-of-sample

1996-1998

1996-2008

Signal

Probit

ANN

Signal

Probit

ANN

20%

% o

f o

bse

rvat

ion

s co

rrec

tly

cal

led

63

.89%

50

.00%

58

.33%

50

.33%

52

.94%

58

.82%

%

of

pre

-cri

sis

per

iod

s co

rrec

tly

cal

led

57

.14%

57

.14%

75

.00%

57

.14%

57

.14%

75

.00%

% o

f tr

anq

uil

per

iod

s co

rrec

tly

cal

led

87

.50%

25

.00%

0.

00%

48

.80%

52

.00%

55

.20%

%

of

fals

e al

arm

s o

f to

tal

alar

ms

5.88

%

27.2

7%

27.5

9%

80.0

0%

78.9

5%

72.7

3%

QP

S

0.72

22

1.00

00

0.83

33

0.99

35

0.94

12

0.82

35

GS

B

0.18

67

0.05

56

0.00

15

0.23

10

0.19

69

0.20

51

30%

% o

f o

bse

rvat

ion

s co

rrec

tly

cal

led

58

.33%

44

.44%

47

.22%

62

.75%

56

.86%

60

.13%

%

of

pre

-cri

sis

per

iod

s co

rrec

tly

cal

led

46

.43%

50

.00%

60

.71%

46

.43%

50

.00%

60

.71%

%

of

tran

qu

il p

erio

ds

corr

ectl

y c

alle

d

100.

00%

25

.00%

0.

00%

66

.40%

58

.40%

60

.00%

% o

f fa

lse

alar

ms

of

tota

l al

arm

s 0.

00%

30

.00%

32

.00%

76

.36%

78

.79%

74

.63%

Q

PS

0.

8333

1.

1111

1.

0556

0.

7451

0.

8628

0.

7974

G

SB

0.

3472

0.

0988

0.

0139

0.

0623

0.

1234

0.

1300

40

%

% o

f o

bse

rvat

ion

s co

rrec

tly

cal

led

30

.56%

41

.67%

47

.22%

73

.20%

60

.13%

62

.09%

%

of

pre

-cri

sis

per

iod

s co

rrec

tly

cal

led

10

.71%

46

.43%

60

.71%

10

.71%

46

.43%

60

.71%

%

of

tran

qu

il p

erio

ds

corr

ectl

y c

alle

d

100.

00%

25

.00%

0.

00%

87

.20%

63

.20%

62

.40%

%

of

fals

e al

arm

s o

f to

tal

alar

ms

0.00

%

31.5

8%

32.0

0%

84.2

1%

77.9

7%

73.4

4%

QP

S

1.38

89

1.16

67

1.05

56

0.53

59

0.79

74

0.75

82

GS

B

0.96

45

0.12

50

0.01

39

0.00

69

0.08

21

0.11

07

50%

% o

f o

bse

rvat

ion

s co

rrec

tly

cal

led

30

.56%

41

.67%

47

.22%

73

.20%

64

.71%

66

.67%

%

of

pre

-cri

sis

per

iod

s co

rrec

tly

cal

led

10

.71%

46

.43%

60

.71%

10

.71%

46

.43%

60

.71%

%

of

tran

qu

il p

erio

ds

corr

ectl

y c

alle

d

100.

00%

25

.00%

0.

00%

87

.20%

68

.80%

68

.00%

%

of

fals

e al

arm

s o

f to

tal

alar

ms

0.00

%

31.5

8%

32.0

0%

84.2

1%

75.0

0%

70.1

8%

QP

S

1.38

89

1.16

67

1.05

56

0.53

59

0.70

59

0.66

67

GS

B

0.96

45

0.12

50

0.01

39

0.00

69

0.04

921

0.07

185

219

CHAPTER 8

CONCLUSIONS

8.1. The Main Findings

The presence and huge impact of currency crises, particularly in the mid-1990s,

encouraged the development of early warning system (EWS) models to predict

these events. This study adds to those efforts by developing three EWS models

for Indonesia, namely the signal approach, the discrete choice probit/logit, and

the artificial neural network (ANN) models. A brief summary of the application

of these three EWS models is also made.

First, following Kaminsky et al. (1998), this study determined that Indonesia

had three currency crises within the sample period (1970-1995) and one

currency crisis, the 1997/98 Asian Financial Crisis, that occurred in the out of

sample period (1996-1998). In predicting these currency crises, the signal

approach was able to predict the 24-month pre-crises periods both in the within

and out-of-sample periods, with the optimum results being obtained at the 30%

cut-off probability point. The weakness in the domestic sector was found to be

the main underlying factor for Indonesia’s currency crises and, based on sector-

specific analysis, it was the financial sector that proved the most dominant in

this respect. Even though it had a limitation when predicting the third in-

sample crisis, this approach was less sensitive to examining the change in crisis

windows, but it consistently performed well when predicting crises within the

sample. However, its out-of-sample prediction was more sensitive, its

predictions not being consistent compared to the benchmark model.

Second, in applying the discrete choice probit/logit model, this study used a set

of explanatory variables based on the top ten indicators when using the noise-

to-signal ratio from the signal approach. Based on the regression results, five

determinant factors were highlighted, namely the short-term capital flows to

GDP, M1 to GDP, loans to deposits, real effective exchange rates and exports,

220

with the short-term capital flows to GDP being a main contributor when

determining the probability of a crisis in Indonesia. In predicting the

Indonesian currency crises, this model was also able to predict both in-sample

and out-of-sample currency crises within the 24-month crisis window. The

sensitivity and consistency of this model varied depending on the prediction

horizons. The longer the crisis window the more consistent was this model in

predicting crises relative to the benchmark model. The performance of this

model in predicting pre-crisis periods tended to increase when the crisis

window was expanded, while the out-of-sample performance was even better

than the benchmark model when predicting results for the 18-month crisis

window.

Third, in predicting currency crises, the ANN model used the same set of input

neurons as the probit model, and showed that the real effective exchange rate

and the 12 months change of loans to deposits were the main factors

contributing towards the probability of a crisis. This model performed well

when predicting the 24-month pre-crises periods for both in-sample and out-of-

sample scenarios. Furthermore, the results of the sensitivity test indicated that

this model was not sensitive to the changes in the crisis window, as it was

consistently able to predict pre-crisis periods, particularly for its in-sample

predictions. Likewise, for the out-of-sample predictions, this test indicated that

the model was less sensitive, but that when compared with the in-sample

prediction, the out-of-sample prediction was more sensitive.

Finally, in comparing the performance of these three EWS models in predicting

currency crises within the 24-month crisis window, it was found that the ANN

model performed better than the other models for both in-sample and out-of-

sample periods. Similar results are found when predictions were made within

shorter crisis windows, over 6, 12 and 18 months, at all level of cut-off

probabilities, these being set at 20, 30, 40 and 50%.

221

8.2. Directions for Future Research

There are several ways in which these EWS models can be improved and

extended in the future. Even though these EWS models were able to predict the

Indonesian currency crises, they also sent lots of false signals, this being

particularly noticeable during the transition and recovery period from 1999 to

2000, during major political events, and during the Global Financial Crisis in

2008. The findings indicate that these EWS models cannot distinguish between

currency crises and other vulnerabilities including political distresses. As this

study only considers the economic and financial variables, so adding variables

such as political risks and the contagion effects may improve the performance

of these models.

The way to define a crisis is also crucial, as the application of the method used

to define a crisis in this study was unable to capture any currency crises after

the Asian Financial Crisis. This was so even though Indonesia experienced

some economic turbulence, particularly during the period of high commodity

prices in 2005, and also during the presence of the global financial crisis at the

end of 2008. One possibility would be to add another variable in the capital

market index. The rationale for adding this variable is that movement in the

capital market index can affect the movement of a domestic currency because of

the integration of capital markets and the trend towards financial liberalization.

222

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