IN THE NAME OF ALLAH THE

MOST GRACIOUS AND

MOST MERCIFUL

WELCOME TO IIUM GARDEN OF KNOWLEDGE AND VIRTUE

BUILDING AN EARLY WARNING SYSTEM FOR

ISLAMIC BANKING CRISIS IN INDONESIA:

SIGNAL APPROACH MODEL

By

Dimas Bagus Wiranata Kusuma and Abu Asif

Candidate Doctor of Economics,

International Islamic University Malaysia

Paper Presented at “Fifth International Conference on

Islamic Banking and Finance” "RISK MANAGEMENT, REGULATION AND SUPERVISION"

CENTRAL BANK OF JORDAN, AMMAN

6-8TH OCTOBER 2012

OUTLINE OF PRESENTATION

INTRODUCTION

PURPOSES OF STUDY

LITERATURE REVIEW

DATA AND METHODOLOGY

RESEARCH FINDINGS

BACKGROUND

CONCLUSION AND RECOMMENDATION

INTRODUCTION

- Islamic Banking (IB) was able to endure the implication of

global crises

- However, given the presence practices of IB has close

connection to the development in conventional banking and

dominantly practicing debt and trade-based financing, it might

create a triggering factor to instability, later trigger

vulnerability into crisis in Islamic banks.

- In fact, banking crisis is costly and might create systemic risk in

economic system, an implementation of EWS for Islamic

banking crisis is very important and calls for existence.

- Therefore, adopts study conducted by Kibritcioglu (2003)

and Kaminski et al (1998), an extraction signal is utilized to

monitor and predict impeding banking sector problem in

Indonesia.

PURPOSES OF THE STUDY 1. To identifies crises periods, or periods of unusual volatility, based on monthly

information for the period from March, 2004 to June, 2012

2. To evaluates the alternative filtering mechanism which leads to the minimum type

I error by employing four selected leading indicators

SIGNIFICANCE OF THE STUDY

- As far as concerned, the study related with EWS model for

Islamic Banking crises is rarely found.

- The paper investigates the predictive ability of the developed model

namely predicting global financial crises in 2008.

- It undertakes a surveillance mechanism for monitoring banking

system operation by exercising conventional selected

macroeconomic indicators

3. To estimate the out-sample data by using in-sample results estimation in order

to testify the reliability of EWS predictability towards the onset of crises

OVERVIEW OF ISLAMIC BANKING DEVELOPMENT

IN INDONESIA

ITEM 2005 JUNE 2012

Islamic banking

network

3 banks and 304 offices 11 banks and 1.529 offices

Islamic Financing

Contract

Sale-Based Contract 63%, and

Equity-based financing 33%

Sale-Based Contract 58%, and

Equity-based financing 28%

Islamic Financing by

sector

Trade, Restaurants and Hotel

11.24%, Business Services 29.49%,

and Agriculture, Forestry and

Agricultural Facilities 4.5%

Trade, Restaurants and Hotel

32.93%, Business Services 12.44%,

and Agriculture, Forestry and

Agricultural Facilities 8.98%

Islamic Banking

Performance

Assets 36.24%, Financing 32.57%,

Deposit 31.36%

Assets 49.16%, Financing 50.56%,

Deposit 51.79%

Financing based on

Type of Usage

Working Capital 52.4%, Investment

28.1%, and Consumption 19.4%

Working Capital 39.63%,

Investment 20.7%, and

Consumption 42.75%

Financing based on

Purpose

66.94% for SMEs and 33.06%

Non SMEs

69.07% SMEs, and 30.93%

Non-SMEs

LITERATURE REVIEW

No Year Focus Method Data Coverage Results

1* 2004 Banking,

Islamic

Banks

Discriminant

Analysis

Total Income/Total Assets, Investment

Income/Total Income, Total

Income/General and Administration

Expenses, Provisions for Bad Debts

and Investment/Total Assets,

Cash/Total Deposits, Customers

Investment Deposits/Shareholders

Equity, Net Profit before Zakat and/or

Taxes/Total Assets

All explanatory variables are significant

to test the classification accuracy ad

prediction reliability of the model.

Incorporating political variables,

macroeconomic variables will improve

the model.

2** 2011 Banking

crises

A review and

Bibliography

of EWS

Sharing some observations regarding a

non-exhaustive collection of the Early

Warning literature from 1971 to 2011,

including evolution of the interest in

EWS, methodology spectrum of

studies, and coverage of economic

variables

The study reveals that (1) EWS seem to

increased after mid-1990s, reached its

climax between 2001-2005, and slightly

decreased afterward, (2) in terms of

methodology, binary dependent variable

family seem to have the most popular.

Individually, logit analysis is the first (21

out 124), signal extraction analysis, and

discriminant/factor analysis share the

second place (14 out of 124).

* Al-Osaimy and Bamakhramah (2004) “ An Early Warning system for Islamic Banks

Performance, JKAU : Islamic Econ. 17 (1) :3-14

** Eray, Yucel (2011) “ A Review and Bibliography of Early Warning Models, MPRA Paper No

32893

DATA AND EMPIRICAL FRAMEWORK

• Time Path of the BSF2 Index and Five Phases of

• A Hypothetical Banking Crisis

BENEFITS OF EXTRACTION SIGNAL OR NON-PARAMETRIC APPROACH

Kibritcioglu (2005) Herrera and Garcia (1999)

1. The Banking Sector Fragility (BSF) Index

which a part of this approach, is very useful

to monitor and interpret the instability in

the sector

2. Easily employed within a single-country

framework

3. Can be used to differentiate “normal” (non-

systemic) and “crisis” (systemic) phase.

1. The simplest approach for EWS

2. Can be updated monthly

3. The lowest feasible cost

4. Can be used to aggregate the individual

leading indicators into a composite index,

and then the index is used as a signaling

device.

Time Path of the BSF2 Index and Five Phases of A Hypothetical Banking Crisis

STAGES FOR BUILDING EWS THROUGH SIGNAL APPROACH

• DEFINING SIGNAL

• BUILDING EVALUATION CRITERIA

– % of observation correctly called, - Noise-to-signal-ratio, - % of crises correctly called, - % of false

alarm of total alarms, - % probability of crisis given an alarm, - % probability of crisis given no alarm

• DETERMINING SIGNALING HORIZON

– Kaminski (1997) defines the period a-priori as 24 months, meanwhile Bussiere and Fratzscher (2002)

set for 12 and 18 months. Those separated horizon is set in order to get the optimal time horizon

• DEFINING CRISES AND THRESHOLDS

– Exercises some threshold levels which are applied by Kaminski (1997), Park (2001), Garcia (1999), and

Lestano (2003), namely respectively 3 standard deviation (SD), 1.1 SD, 1.5 SD, and 1.0 SD.

– The crises periods is defined as a period in which Islamic banking sector fragility (IBSF) > μ+m𝝈

(where μ is the sample mean and 𝝈 the standard deviation of the IBSF, and m the threshold levels).

• DEFINING AND CONSTRUCTING ISLAMIC BANKING CRISES INDEX

– This paper proposes IBSF2 (general index) to measure the fragility of banks to crisis, namely:

Table 10. Possible Scenarios of Signals and Crisis

Crisis No Crisis

Signal Issued A B

No Signal Issued C D

IBSF2 = Islamic Banking

Sector Fragility,

DEP=Deposit, DC=Domestic

Financing, , μ= mean, 𝝈=standard deviation

• We differentiate the degree of

crisis into three types of fragility

values (Kibritcioglu, 2005)

• While, an Islamic banking

system is supposed to be in a

medium fragility period, if the

value of the IBSF2 index is

between 0 and -0.5

(-0.5<IBSF2t<0).

• Then, if the value of the IBSF2

index is lower than -0.5, we

assume that the relevant banking

sector is highly fragile to have

systemic crisis (-0.5>IBSF2t).

Normal banking crisis defined as

the IBSF2 does not deviate

significantly from zero. Therefore,

there is no reason to expect a severe

banking sector problem in the short

run, (10.1>IBSF2t<0.1 )

- Selecting Leading Indicators of Crises (Herrera and Garcia, 1999)

- M2/Reserve Growth

- Domestic Credit Growth

- Real Effective Exchange Rate

- Inflation Rate

DATA AND EMPIRICAL FRAMEWORK

DEFINING DEGREE OF CRISES

RESEARCH FINDINGS TIME PATH OF THE IBSF2 INDEX AND THE PHASES IN INDONESIA

MARCH 2004-DEC 2006

• According to above figures, we can interpret

that for initial month in 2004, Islamic banking

in Indonesia were excessively risk taking

• During early seven months in 2004, the direction

of IBSF2 increases significantly above zero, and

expected to fall. In fact, since September 2004,

IBSF2 index suddenly begins to decrease and

implies Islamic bank generally are starting to

risk avoiding

• This index continues to decrease and falls below

-0.5 since July 2005. Therefore, since July 2005

to December 2006, IBSF2 index shows some

indications for the upcoming of banking crises

or systemic banking crises

• Therefore, empirically, this type of index is

capable of providing more information about the

ups and downs in the Islamic banking sector

with respect to certain crisis-years in event-

based studies

RESULTS IN-SAMPLE DATA

Signal-Generating Mechanism and Determining Leading Indicator of Crisis

All indicators show the lowest noise to signal ratio (NTS) which mean showing

the best threshold chosen (Garcia) to minimize type I error (probability of not

anticipating a crisis) and reduce substantially cost of not anticipating the extreme

risk situations. Meanwhile, this paper also finds that 24 month signal horizon is

the best because it would bring the employed indicators to have best ability for

anticipating crises.

No Variable Threshold Signal Horizon Evaluation Criteria

1 M2/Reserve

Growth

Garcia 24 month The highest percentage in terms of

% of observation correctly called

and lowest NTS

2 Domestic

Credit Growth

Garcia 24 Month The lowest NTS

3 Real Effective

Exchange Rate

Garcia 24 Month The lowest NTS, the lowest % false

alarm of total alarm, and highest %

probability of crisis given an alarm

4 Inflation Rate Garcia 24 Month The lowest NTS, highest percentage

of crises correctly called as well as %

probability of crisis given an alarm

RESULTS OUT-SAMPLE DATA

No `Category

REER

(Real Effective

Exchange Rate)

M2/

Reserve

Credit

Growth Inflation

1 NTS (Noise To Signal

Ratio) 0 0 0 0

2

% Of Obs.

Correctly Called

0.8628

0.8633

0.9858

0.7028

3 % Prob. Of Crisis

given No Alarm 0.1330 0.1518 0.1031 0.1522

The out-of sample estimation posit several interesting results

1. All selected leading indicators are able to minimize the type 1 error. It implies

the model outperforms for capturing and explaining the existing crises in Islamic

banking industry

2. All selected variables are able to explain the incoming crises once the signal

issued

3. The model is able to reduce the possibility a crisis occur without signals

CONCLUSION

• The IBSF2 which was developed in this study is able to figure out the

development process of Islamic banking crises in Indonesia over March 2004

to December 2006

• By utilizing signal generating mechanism, The results find that four leading

indicators could be used as best predictor variables for Islamic Banking crisis if

the threshold set is Garcia approach with 24 month signal horizon

• By exercising the out-of-sample period and employing Garcia approach, the

study deduces that all selected leading indicators vindicate the ability for

correctly forecasting the crises occurrence with at least 24 months before the onset

of crises.

RECOMMENDATION

• The call for more leading indicators and more complicated methods or

approaches is highly needed. In the case of conventional banking, for

instance, more macro and micro prudential leading indicators are exercised. In

terms of methodology, the use of logit/probit model, artificial neural network,

and Markov Switching model are empirically encouraged as surveillance

mechanism

THANK YOU FOR YOUR PRECIOUS TIME

MAY ALLAH BLESS US

WITH KNOWLEDGE AND WISDOM

WASSALAM