NATIONALEKONOMISKA INSTITUTIONEN Uppsala Universitet Examensarbete D Författare: Gabriel Söderberg Handledare: Nils Gottfries Termin och år: Vårterminen 2005
Predicting Currency Crises – Possible or Impossible?
Abstract
This paper is an assessment of the possibility to predict currency crises. Different methods are
explored. A discrete-choice model is estimated with an underlying intuition that is far more
simple than traditional estimations of this kind. The results suggest that currency crises are
complex phenomenas that cannot be predicted by just using a few variables. I then turn to a
descriptive analysis, that first focuses on macroeconomic fundamentals and then on domestic
troubles in the banking sectors of crisis-struck countries. Finally some possible contributing
factors lying outside the control of crisis countries are discussed. The final conclusion is that
the exact timing of a currency crisis cannot be predicted, but that vulnerability – that is a high
probablity that a crisis can occur – can be detected. Further research on what finally triggers a
crisis in a vulnerable situation is therefore needed.
Acknowledgements
I would like to thank the following people (in alphabetical order): Nils Gottfries for generally
being a good supervisor; Per Johansson for statistical support; Peter Nilsson for useful
suggestions and statistical support; Erik Post for literature tips
2
Contents
1. Introduction...........................................................................................................................4
2. Theoretical models................................................................................................................5
2.1 First generation...................................................................................................................5
2.2 Second generation...............................................................................................................6
2.3 Third generation..................................................................................................................6
3. Discrete-choice regression....................................................................................................7
3.1 Methodology.......................................................................................................................7
3.2 Empirical specifications......................................................................................................8
3.3 Data&sources......................................................................................................................9
3.4 Results...............................................................................................................................11
4. Descriptive analysis.............................................................................................................13
4.1 Macroeconomic fundamentals..........................................................................................14
4.2 The banking sector............................................................................................................22
4.3 External factors.................................................................................................................31
5. Conclusion and summary...................................................................................................35
References................................................................................................................................37
Appendix..................................................................................................................................40
3
1. Introduction The last decades have seen the frustration of many countries as they battle against pressure on
their fixed exchange rates. Often this pressure is overpowering, forcing the country to devalue
or let the currency float. The costs involved in these crises can potentially be enormous.
Indonesia for instance lost 13.5 percent of its GDP the year following the crisis 19971, and the
portion of the population qualified as poor increased from 12 to 22 percent.2 With the 1998
population of Indonesia at 213 millions, this means that approximately 21.3 million people
were thrown into poverty. Learning how and why such crises occur, is thus of extreme
importance. And much more important still: if it is possible to predict that given the current
situation a crisis will occur, it might theoretically be possible to prevent it. This paper is an
attempt to evaluate this possibility to predict a currency crisis.
Since the late 1970’s economists have used different approaches for this end.
Each “wave” of crises spawned its very own generation of theoretical models.3 Models in
each generation all share similar assumptions based on the types of crises they were
constructed to explain.Thus the “first generation” models are based on the Latin American
experience of the late seventies and early eighties, the second on the European and Mexican
crises of 1992 and 1995, and the third generation more or less on the Asian crisis of 1997.
More recently a fourth generation is also being introduced. This constant creation of new
models is a reflection of the complex and versatile structure of currency crises. One
explanation is simply not enough, and each of the models that have been suggested has its
own flaws.
In part as a response to the empirical troubles of the theoretical models, the so
called “leading indicator” approach has been suggested.4 In this the researcher tries different
means to measure a country’s vulnerability to a crisis, or to predict one, using a set of
indicators that reflects all possible causes of the theoretical models mentioned above. This
approach thus tries to “side step” the theoretical literature, instead using some sort of
underlying intuition in choosing its indicators. There are three main methods for doing this.
The signaling method (i) tries to find a crisis “alarm clock” that goes off when one or more
variables exceed a certain threshold. The discrete-choice method (ii) instead uses binary
1 Perkins et al (2001) 2 There might be a problem of causality here: does the crisis cause the downfall or does the downfall cause the crisis? 3 Breuer(2004) 4 Chui(2002)
4
regression models in order to estimate the probability that a crisis occurs given a set of
indicators. And finally the third method is a more descriptive analysis of different case studies
(iii), sometimes by creating a statistical model but often by non-quantitative evaluations of a
country’s situation.
In this paper – as means to assess the possibility to predict currency crises – the
discrete-choice and the descriptive methods will be used. First two separate logit models are
estimated – one for developing and one for industrialized countries. I then turn to a
descriptive analysis first of macroeconomic fundamentals and then on the general status of the
banking sector in different countries. This yields the conclusion that the exact timing of a
currency crisis is probably impossible to predict, but that vulnerability to crisis should be
possible to detect. Therefore predicting crisis seems to be equivalent to finding situations
were crises can suddenly erupt, although the triggering causes are not fully understood at this
time.
The rest of this paper is organized as follows. In section 2, I briefly describe the
theoretical models. Section 3 describes and presents the results of a discrete-choice
regression. Section 4 is a descriptive analysis of a number of cases, analyzing whether the
crisis in Asia 1997 could have been predicted by finding similarities with the then rather
recently crisis-struck countries of Finland, Mexico, and Sweden. Finally, section 5 includes
summary and a conclusion.
2.Theoretical models There are mainly three different types of models, often called the first, second, and third
generations. Each of these generations contains a number of different models that share
several similarities, and are generally based on a certain wave of crises that hit a region in a
particular time period. I here very briefly describe these three generations.5
2.1 First generation
The experiences of the Latin American countries in the 1970’s and 1980’s served as
foundation for the earliest models. These countries typically had large budget deficits which
the government financed by borrowing from the central bank. This together with large current
account deficits followed by large capital outflows led to problems that ended with a drop in
the fixed rate. The first generation models thus try to explain how and why this happens. For
5 The following is based on Breuer(2004) and Saxena(2004)
5
example the Krugman model (1979), assumes that the government is financing its debt by
borrowing from the central bank while the central bank must use its foreign reserve to keep
the exchange rate at a fixed level. This situation is unsustainable: sooner or later the central
bank must run out of foreign currency. Speculators – assumed to have perfect foresight –
realize this and will stage an attack in order to profit from the discrete jump in the exchange
rate as it is depreciated. Competition among these speculators, however, pushes the timing of
the attack backwards, so that it will occur before the central bank has run out of reserves. This
means that there is no jump in the exchange rate although it starts to depreciate continuously.
2.2 Second generation
The European crises in 1992 and 1993 were different from the ones in Latin America. There
were no severe macroeconomic problems, and no inconsistencies between the keeping of a
fixed exchange rate and financing of the budget deficit. Instead these countries were hit by
massive speculative attacks, leading them to devaluate or float their currencies long before
their central banks were insolvent. Most of these countries had the means to continue
protecting their exchange rates but chose not to. The prevalent model – Obstfeld (1994) - thus
focuses on self-fulfilling crises, that is crises created by people’s expectations, and the fact
that the government chooses whether to defend the exchange rate or not out of the perspective
of optimizing utility. Currency crisis can therefore theoretically occur in a relatively good
economic environment, simply because pessimistic investors withdraw their money and the
government finds that protecting the exchange rate is more costly than letting it go.
2.3 Third generation
The Asian crises in 1997, and the fact that none of the earlier models were sufficient to
explain it, created interest in the workings of the banking sector. Thus the “twin crisis”
concept was introduced, underlining the connection between banking and currency crises.
Here there is a huge number of different models and no one has become prevalent in the way
that the Krugman and Obstfeld models have. Often these models cointain elements from the
earlier generations, mainly the concept self-fulfilling crisis.6 Most of them focus on over-
lending to risky projects which makes the country vulnerable to external shocks. In this way
the occurrence of a banking crisis – a large amount of non-performing loans that hurt the
banking sector – can trigger a currency crisis for example through capital flight as investors
6 Chui(2002)
6
realize the weakness of the country. Also a situation with highly debt-levered firms raises the
cost for protecting the fixed exchange rate through interest rate increases and thus increasing
the government incentive to devalue if faced with pressure. This might lead to a speculative
attack.
3. Discrete-choice regression This section describes a binary response approach to the problem of predicting currency
crises.
3.1 Methodology
In order to asess the main hypothetical causes of currency crises I estimate a regression model
based on a balanced panel of countries. This takes the shape of a logit model which, given the
indicators discussed below, estimates the probablity that a crisis occurs. This probability is
assumed to follow the logistic cumulative distribution function, that is,
iXii e
XYEP β−+===
11)1( , (1)
where P is the probability of crisis a particular year, X is the transposed vector of all
independent variables, β a vector of coefficients, and Y takes on the value one if a crisis
occurs. Dividing this by (1-P) gives the odds ratio, which, by taking logarithms, is simply
expressed as,
iX
XX
ii Xe
ee
XYELi
i
i
ββ
ββ
=⎟⎠⎞
⎜⎝⎛=
⎟⎟⎟⎟
⎠
⎞
⎜⎜⎜⎜
⎝
⎛
+−+
=== −
−−
1ln)
111)(1(
1ln)1( , (2)
where L is the logarithm of the odds ratio. In other words, the function has been linearized
which allows different types of standard operations. Using annual observations for a sample
of countries with Y=1 indicating a crisis year and Y=0 a non-crisis year, it is now possible to
estimate the parameters of this function. By including fixed effects in the estimations,
variations among different countries are included.
7
3.2 Empirical specifications
Earlier theories and observations leads me to focus on four factors: current
account deficits, government budget deficits, poor growth of domestic output, and worsening
international competitiveness. Current account and budget deficits are scaled by expressing
them as percentage of GDP. The other two hypothesized causes are trickier to deal with. In
order to model the impact of possible problems within the domestic economy, I created an
index of actual output compared to its trend. This “output gap” is defined as the difference
between the logarithm of real GDP and its Hodrick-Prescott trend. Also, international
competitiveness is measured by the percent increase in real effective exchange rate over a
period of three years7. The included indicators and their theoretical motivations are thus as
follows. Current account: A current account deficit means that a country is having a foreign
debt that might lead to strain on the currency reserves if creditors choose to withdraw their
investments. Budget balance: A budget deficit might mean that the government is tempted to
use inflation as a source of revenue, also putting pressure on the foreign reserves. Output gap:
Low domestic output means a signal of economic weakness, suggesting to investors that the
country is not worthy of the funds invested in it. Real effective exchange rate appreciation:
Worsening competitiveness might suggest that a devaluation is needed in order to put the
export industry back on track.
All indicators were lagged one period in the regression. Thus the function to
be estimated is:
ttt
ttt
tt
REERGAP
GDPBBGDPCAP
PL
εββ
ββα
+∆++
++=−
=
−−
−−
1413
12111 //1
ˆ (3)
where (CA/GDP) denotes current account as percentage of GDP, (BB/GDP) government
budget balance as percentage of GDP, GAP the output gap, and ∆REER the percent three
year change in real effective exchange rate8.
Table 1 summarizes the expected signs of the estimated coefficients. The current
account as percentage of GDP should be negative, that is a deficit increases the chance of a
crisis. For the same reason budget balance as percentage of GDP and output gap should have
7 The real effective exchange rate is calculated by giving other countries a weight according to their importance as trade partners. 8 Estimating with fixed effects means giving each country but one a dummy variable that influences the intercept. The full function to be estimated is thus:
tttnnt REERGAPGDPBBGDPCADDL εββββααα +∆+++++++= −−− 1413211121 //...ˆ
8
negative coefficients. The coefficient for the real effective exchange, though, should be
positive since a high value on this suggests hard times for the export industry.
Table 1. Expected signs of estimated coefficients
Indicator Expected sign
CA/GDP -
BB/GDP -
GAP -
∆REER +
3.3 Data&sources
My approach to the discrete-choice method of predicting currency crises, and therefore the
underlying causes, is in several ways different from similar estimations in the past. First of all,
it is very parsimonious: I include only four explaining variables, whereas Frankel and Rose
(1996) include seventeen, Kumar, Moorthy, and Perraudin (1998) thirty-two, and
Komulainen (2004) twenty-three. Secondly, my model – in relation to its modest size – is
based on the simplest possible intuition. The underlying intuitions in other empirical models
are far more complex, including such variables as short term debt, central bank currency
reserves, and foreign direct investment. One can be sceptical about these approaches, since
including so many different variables entails great risk of multicollinearity. Also, variables
with weak intuitive justification are sometimes included. Thus, if the estimated parsimonous
model is inadequate to predict crises, the use of more complex models is to some extent
justified.
Thirdly, there is the difficult question of defining a crisis. This generally
includes some arbitrariness. Frankel and Rose for instance use a more than twenty-five
percent depreciation of the nominal exchange rate together with an increase of ten percent in
the rate of depreciation. I instead use a depreciation of more than eight percent of the nominal
effective exchange rate for a country with fixed exchange rate. This value was chosen after
reviewing “known” crises and their percent depreciations. Still the reader should bear in mind
that both my approach and the others are bound to miss some crises, since the floating of a
previously fixed exchange rate does not necessarily mean that it depreciates to a great extent.
The French experience during 1992 is an example of this situation: the country was forced to
9
abandon its peg and let the Franc float, but its nominal value only depreciated a few
percentage points. This crisis can not be detected using a measure of depreciation. Other
definitions have similar types of problems.
I estimate two different panel regression models: one for industrialized
countries, and one for developing countries. The reason for this is that one could assume that
these two groups would behave differently when faced with crisis pressure and thus be
inequally sensitive. An industrialized country might for example avoid crisis in spite of a huge
current account or budget deficit while a development country might not. Investors might for
example have lower confidence in the ability of developing countries to deal with their
problems and thus withdraw their money before assumed chaos erupts. Thus it seems a good
idea to keep these two groups separate, although still including newly industrialized countries,
such as South Korea, in the development country category.
In both groups I only include periods with fixed exchange rate regimes. The
definitions of these are taken from Reinhart and Rogoff (2002). I characterize “fixed
exchange rate regime” as everything apart from managed floating and freely floating, that is I
include moving pegs, bands around other currencies, and so on. None of the other studies
seem to have done this, trading a greater number of observations against the unreliability of
including observations where a currency crisis in the usual sense cannot occur – simply
because there is no peg to come under pressure.
The quality of the data can be questioned. For many countries – especially
developing countries – there are large gaps in the time series. Furthermore there are a few
extremely peculiar observations that seem safer to remove than to include in an estimation.
Concerning the frequency of observations used, several researchers – among
them Kumar et al and Komulainen – suggest that monthly data better capture the volatile
nature of financial crises. Most of the data are however only available in annual or quarterly
frequency. The observations used in models with monthly frequence are interpolated. While
this is “allowed” I still feel – as do Frankel and Rose – that the benefits of manipulating the
data in this way do not outweigh the potential drawbacks of it. In the words of Gujarati9: “All
such data ‘massaging’ techniques might impose upon the data a systematic pattern that might
not exist in the original data.”
The complete data set used consisted of 15 developing countries, and 13
developed countries, containing time series of the chosen indicators stretching from 1970 to
9 Gujarati(2003)
10
2000. Most of it was taken from IFS Financial Statistics, while some of the development
countries’ real effective exchange rates were taken from J.P Morgan10. Descriptive statistics
and a full list of countries included are found in the appendix.
3.4 Results
The results are somewhat disappointing. In the developing country estimation, only the
intercept and deficit as percentage of GDP are statistically significant, while only the intercept
is significant for industrialized countries. However, the null hypothesis that all variables
collectively have no impact on the probablity of crisis occuring in developing countries is
rejected at 10% level of significance. The same does not hold for industrializes countries
though. In both cases, furthermore, the measure of goodness of fit – McFadden R² - is very
low. The result is summarized in table 2 and 3.
Table 2: Results for Developing Countries
Variable Coefficient (s.e) P-value11 Estimated sign (and
expected)
C -1.205 (0.244) 0.000***
CA/GDP 3.155(4.545) 0.488 +
(-)
BB/GDP -8.567(4.003) 0.0324** -
(-)
GAP 3.714 (7.130) 0.603 +
(-)
∆REER 0.015336 (0.0110) 0.161 +
(+)
P-value (LR-stat) 0.0984* McFadden R² 0.0496
Observations
included: 137
10 The exact definitions in IFS are: CA/GDP=line 78ald/line 99b, BB/GDP=line 80g/line 99b, GDP in fixed prices=lines 99b.p or 99b.r, REER= line rec 11 *** indicates 1% significance, ** 5%, and * 10%
11
Table 3: Results for Industrialized Countries
Variable Coefficient (s.e) P-value Estimated sign (and
expected)
C -2.187 (0.370) 0.0000***
CA/GDP
-5.0469 (9.0421) 0.5767
-
(-)
BB/GDP
-1.502 (7.430) 0.8398
-
(-)
GAP
7.816 (11.307) 0.4894
+
(-)
∆REER
0.00922 (0.0161) 0.5681
-
(+)
P-value (LR-stat) 0.831 McFadden R² 0.0118
Observations
included: 180
In other words: with given data and model specification, the results show that it is not possible
to satisfactorily explain or predict a crisis. Why might this be so? The most obvious
explanation is miss-specification. The indicators involved are simply not enough to explain
the difference between crisis and non-crisis years. Of course, as Gujarati puts it, “searching
for the correct model is like searching for the Holy Grail”.12 But it still seems that the “correct
model” is a more complex one, involving far more indicators and a higher level of complexity
than the one estimated here. I therefore draw the conclusion that the complex models of
earlier estimations are partially justified in their inclusions of large amounts of variables.
Another highly plausible explanation is lack of data and/or the quality of it.
Aside from the removal of observations during non-fixed exchange rate years, there were also
considerable gaps in the time series. Including many more countries to increase sample size is
a problem since one simply “runs out of” industrialized countries, and the quality of the data 12 Gujarati(2003)
12
detoriates when dealing with smaller and smaller developing countries. Another possible
solution is to estimate the model on a data set consisting of both developed and developing
countries. I did this – including 317 observations – but the results were rather similar to the
ones above.
Since few or none of the indicators were statistically significant analyzing their
estimated coefficent might be a worthless exercise. Still it might be interesting to see if their
signs and sizes are consistent with intution. The only significant indicator – the budget
balance of developing countries – is negative as expected, as is its industrialized counterpart.
The current account however only has the expected sign in the case of industrialized
countries, while outputgap in both cases are positive instead of negative as expected. Finally
the percent increase in real effective exchange rate has expected sign in both estimations.
4. Descriptive analysis The results of the discrete-choice models highlight an important lesson: the phenomenon of
currency crisis is a very complex one. In this section the possibility of prediction is analyzed
in a more qualitiative way.
In this part of the study I will investigate if the crisis in Asia 1997 could have
been predicted. This crisis is interesting mainly for two reasons. First, it was a complete
anomaly in the framework of existing theoretical models13, and thus impossible to predict
applying these. Second, it set off a series of currency crises that followed in its wake – Russia
1998, Argentina and Brazil in 1999. The method used is to compare Asian characteristics the
years preceeding the crisis with those of other countries earlier hit by crises. To this end I
have chosen three reference points: Finland 1992, Mexico 1994, and Sweden 1992. The
reason for choosing these countries is deliberate, since a superficial glance at the crises in
these countries suggests some similarites that should be explored. Given what we knew from
these countries, could the crisis in Asia have been predicted? If this is the case, there have to
important similarities between what happened in Asia 1997 and what happened in Europe
1992 and/or Mexico 1994. Finding such similarities is thus the main aim of my study.
First I evaluate the macroeconomic fundamentals that were extant the years
before the crisis. Then I analyze the suggestions prevalent in third generation models that the
crisis was linked to problems in the banking sector. After that I consider various events lying
outside the control of the crisis countries, such as random events and policy changes in other
13 Saxena(2004)
13
countries. Throughout, the Asian countries will be represented by Thailand. This is for two
reasons. First, the crisis began in Thailand which might mean that all the rest of the countries
were affected in a process of contamination. Second, before the crisis the Thai policies on
attracting foreign capital were widely copied in Asian countries14. Each following sector will
first describe the Thai characteristic, and then more briefly those of the other countries.
4.1 Macroeconomic fundamentals
The East Asian crisis is famous for occurring in a region with stable macroeconomic
indicators. In December 1996 an IMF report titled “Thailand: The Road to Sustained Growth”
apart from a considerable current account deficit found no cause to worry about the country’s
future.15 This was 3 months before speculators attacked the Thai currency in February 199716
- the first of a series of attacks that would finally force the country off its peg. Facts like these
have made some researchers doubt “the usefulness of...macroeconomic variables as warning
signals of vulnerability”.17 I will here investigate this suggestion.
In some cases countries’ paths towards currency crisis from the late 70’s and
onwards had been clearly visible in macroeconomic indicators.18 First the Latin American
experiences, with huge budget deficits financed by high growth rate of money and massive
current account deficits. Then the European crises of the early 90’s typically with
“manageable” budget and current account deficits, but with increasing problems with
unemployment and competitiveness due to falling demand and real exchange rate
appreciations. Finally the Mexican crisis only two years before the Asian, which was
preceded by years of budget surplus, but high inflation, real exchange rate appreciation,
falling growth rate, negative trade balance, and large current account deficits.19
The fundamentals chosen here are: budget balance, current account, growth rate
of GDP, and real effective exchange rate.
Budget balance
First of all Thailand had no problems with government budget deficits. On the contrary, the
Thai government followed an unusually strict spending policy20, perhaps trying to avoid the
14 Allen(1999) 15 Dornbusch(1997) 16 Lauridsen(1998) 17 Lim et al(2004) 18 Saxena(2004) 19 Dornbusch et al(1995) 20 Dornbusch(1997)
14
problems experienced by earlier emerging economies in Latin America. Far from budget
deficits, the 90’s was a period of constant surpluses as shown by figure 1 until the crisis hit in
1997.
Figure 1: Budget balance relative to GDP21
The Thai budget situation during the 90’s was very different from the experiences of the Latin
American countries in the 70’s and 80’s. Using the framework of the first generation it would
indeed have been impossible to predict the crisis in 1997. But later experiences should have
suggested that a well-kept government budget does not make a country immune to crises. Of
all the four countries analyzed here, only Finland had a large budget deficit the year of the
currency crisis but this had been preceeded by a number of years with manageable deficits
and even surpluses in the late 80’s. Sweden had a small surplus until 1990 and then aquired a 21 Source: IFS (line 80g/line 99b)
15
deficit that passed 4% of GDP in 1992. But this is still managable. In contrast the first
generation crisis wave involved far larger deficits - Bolivia being an extreme case with a
deficit of 40% during the 1982-1985 crisis22. Viewing the graph describing the Mexican
budget balance further adds to the conslusion that a country without budget deficits is not
safe: the years before 1994 show small surpluses, changing into a minuscule deficit in the
year in question.
Thus while a good sign that Thailand was not susceptible to the debt-caused
crises of the first generation, it should have been obvious that budget surpluses in itself was
not enough to stop a crisis from occurring.
Current account
On the other hand Thailand had considerable current account deficits. This was due mainly to
the vast amounts of foreign capital flowing into the country following its financial
deregulations. The way this inflow was handled and the impacts of it will be discussed in the
next section. For now, viewing the graph below shows that Thailand was running a large
constant current account deficit during the entire 90’s.
22 Saxena(2004)
16
Figure 2: CA relative to GDP23
A large current account deficit is of course not necessarily a problem as long as the debt is
used to finance useful upgrades of productivity and competitiveness. If GDP increases it is
even possible to maintain a constant debt ratio in spite of increasing capital inflows. But it still
implies a vulnerability to sudden reversals if for some reason the lenders decide to withdraw
their money. Comparing with the three other countries, we see that all of them had growing
current account deficits, especially Mexico passing 10% of GDP in 1994. However the
Finnish and Swedish deficits are not extremely large – with the Swedish deficit at its absolute
low point being roughly half the average Thai for several years - suggesting that a constant
deficit of around 8% of GDP as in the case of Thailand might potentially make the country
vulnerable. If one adds the assumption that foreign lenders are more sensitive when investing
in developing countries, this further adds to the potential hazard of the situation. On the other 23 Source: IFS (line 78ald/line 99b)
17
hand Thailand had been running this deficit for years. A large deficit does not necessarily
have to lead to crisis.
Growth rate of GDP
Both from theory and observation we know that a situation with huge current account deficits
can become a problem if the lenders lose confidence in the country’s ability to repay its debts
in the future or maintain high returns. The indebted country will therefore need to show strong
growth and competitiveness statistics to prove that it is indeed worthy of the capital invested
in it. Also speculators considering launching an attack might draw the conclusion that the
country cannot allow itself to raise interest rates in order to protect the fixed exchange rate.
Looking at the growth rate of GDP for Thailand, one can see that the rate was increasing
slightly for several years and then dropped rather drastically the year before the crisis, from
9.2% to 5.9%.
18
Figure 3: Growth rate of GDP24
The Thai downfall in growth rate might suggest that 1996 was the year in which investors lost
confidence in the country’s ability to handle its debt. But although it fell, a growth rate of
5,9% is a rather healthy one. Finland and Sweden in contrast had much more severe problems.
We see in the graphs that the sharp downfall of growth rate that appeared after the crisis in
Thailand and Mexico appeared before in Finland and Sweden. But although Finland had a
devaluation in 1991 the speculative attacks did not occur until 1992. In Mexico the situation
was slightly different as seen from its graph. Here there was a slow growth rate for many
years and the inflow of capital does not seem to have been able to speed up development.
Thus the slow growth pace of Mexican economic activity was probably one of the reasons for
the problems in 1994.
Real effective exchange rate
24 Source IFS (Lines 99 b.p and 99 b.r)
19
Finally there is the question of an over-valued exchange rate, which reduces international
competitiveness. In several European countries of the early 90’s, with currencies pegged to
the D-mark, inflation differentials relative to Germany caused domestic real exchange rates to
appreciate around 1990.25 In such a situation, a devaluation can be necessary to promote
export activities and raising interest rates in order to maintain the peg may be too costly.
Looking at the Thai real effective exchange rate, it is indeed evident that the country was
suffering from a creeping appreciation that undermined its competitiveness.
Figure 4: Real effective exchange rate26
The Thai appreciation was mainly due to both inflation differentials with the United States27,
and the appreciation of the dollar during the 90’s. Since the Thai baht was pegged to the
dollar the real exchange rate appreciated both against the dollar and most of the other larger
25 Saxena(2004) 26 Source: IFS (line re c) 27 Andersson et al (2002)
20
currencies. This led to harder times for the export industry, as its main advantage of low
wages was undermined, probably contributing to the drop in growth rate we saw in the
previous graph. Finland and Sweden had appreciations in the late 80’s, but it would be wrong
to ascribe all of the following downfall in growth rate to worsening competitiveness, since
there were also problems with falling demand in importing countries – due in large part to the
fall of the Soviet Union, a major trade partner. Visible here is also the Finnish devaluation in
1991. In the case of Mexico, the appreciation was far more severe. The real effective
exchange rate had appreciated rapidly the years before the crisis. This was not enough to send
the growth rate down to negative numbers, but the slow growth rate might well to a great deal
be explained by this, as the stimulation of a profitable export was undermined
Summary and comparison
Comparing Thailand, Finland, Mexico, and Sweden shows several similarities: sound budget
balance (with the exclusion of Finland), current account deficits, some sort of trouble with
growth rate, and real effective exchange rate appreciations. I therefore do not agree that
analyzing the Thai fundamentals “leads one to question the usefulness of...macroeconomic
variables as warning signals of vulnerability”.28 On the contrary, if one defines “vulnerability”
as a setup of macroeconomic variables similar to the setup of a country that afterwards was hit
by a crisis, then I would say that Thailand was indeed becoming vulnerable. Table 5
summarizes some of the similarities.
Table 4: Similarities of compared countries Country Large or
growing budget deficit
Large or growing current account deficit
Low or falling growth rate
Loss of competitiveness
Thailand No Yes Yes Yes Mexico No Yes Yes Yes Sweden No Yes Yes Yes Finland Yes Yes Yes Yes
However, there is a big difference and that is the early response of speculators and investors
following visible troubles. Finland and Sweden had had severe problems since 1990, and
Mexico had had slow growth for several years, while the troubles in Thailand only started to
manifest themselves one year before the crisis. I must therefore conclude that even though
fundamentals suggest that Thailand was heading for a crisis, they are not enough to explain
28 Lim et al(2004)
21
why it finally occurred when it did. From analyzing just fundamentals in 1996, one would
have thought that Thailand had at least two or three years before crisis, while speaking about
“sustained growth” would have been wishful thinking.
4.2 The Banking Sector
Following the Asian crisis was a considerable number of analyses to discern why it had
occurred in spite of manageable macroeconomics.29 Amid many suggested explanations, the
main discussion soon focused on links between banking and currency crises. The banking
sector in the Asian countries was having severe problems, manifested in large amounts of
non-performing loans that sent many banks into bankrupcies. The abundance of theoretical
third generation models make different suggestions on the nature of this link, but in general
one could assume that the correllation is due to three things. First, turmoil in the banking
sector might accentuate troubles in the country which scares away investors.30 Second, raising
interest rates in order to protect a fixed exchange rate in a capital flight, might hurt firms with
high debt-leverage which might further increase the amounts of non-performing loans making
the problems even worse. Thus, just as in the case of a recession, the cost of raising the
interest rate can be prohibitingly large, signalling that a speculative attack is a good idea.
Third, government bail-outs of insolvent banks can increase inflation. Therefore the presence
of weak banks might increase inflation expectations, raising costs of maintaining the peg
through higher wage settings.
Was East Asia rotten inside, just waiting to collapse in spite of manageable
macroeconomics? I here follow the same procedure as earlier. First I describe the situation in
Thailand, and then I briefly discuss the other countries. Finally, I compare the experiences of
the countries and assess whether the vulnerability in Asia could have been predicted by
looking at the past.
The following is an overview of existing literature on the subject. Since each
description is based on its own setup of sources, that all have focused on different aspects and
observations, this section will be rather assymetric. The described cases are separate stories,
which at the end of the section will be bound together in a study of similarities.
Thailand31
29 As mentioned earlier, the situation was deteriorating though. 30 Saxena(2004) 31 The following is based on Alba(1999), Andersson et al(2002), and Lauridsen(1998)
22
In the late 1980’s the Thai authorities launched a program of extensive liberalization of both
capital accounts and the financial system. Already a rather “open” country in 1985, the
restrictions on foreign borrowing, lending, and ownership were further loosened. This was in
accommodation of both foreign eagerness to invest in a promising country, and the domestic
wish to fuel an already rapid growth rate. These liberalizations were formalized in 1990, when
Thailand officially accepted the IMF article VIII obligations. Also, as an attempt to turn the
capital of Bangkok into a financial centre the Bangkok International Banking Facility (BIBF)
was established in 1993. This heavily subsidized institution, aimed at creating a financial
infrastructure for international banking activities, was to have a great impact on the
composition of external debt. Most of the funding of licensed banks operating under the BIBF
was short-term and foreign. The tax-exempted status of these banks meant that this short-term
capital with lower borrowing costs replaced other types of debt, thereby increasing the
proportion of short-term capital flows. At the same time the authorities were implementing a
deep-going financial reform in order to make sure that enough capital was available. In a
series of reforms the interest rate ceilings of banks were removed, allowing the rate to be set
by supply and demand. Furthermore the restrictions of financial institutions were loosened up.
The result was sharply increased credit extension. Largely due to the BIBF most
of the capital was foreign. From a level of of 59.1 % of GDP in 1988, the foreign debt
proportion rose to 94.1% in the end of 1997.32 Again to a large extent due to the BIBF, the
proportion of short-term debt increased rapidly. The Thai authorities realized that this might
pose a problem in the future and tried to lessen this portion. Table 6 shows both the increase
in private debt and in the short-term proportion of it from 1993 when the BIBF started to
operate. Also note the decrease in 1996 in short-term private debt, suggesting that the attempt
to lower this proportion was somewhat successful.
Table 5: Private debt
Debt 1993 1994 1995 1996
Total(% of GDP) 30% 34% 39% 41%
Short-term(% of GDP) 18% 20% 24% 21%
Source: Alba (1999)
32 Alba(1999).
23
Who were the people that took these loans? Afterwards it is evident that the financial
institutions in Thailand were not prepared for the challenges of selecting and monitoring the
projects applying for loans. Evaluating the risk of potential projects as well as monitoring
these after the loan has been granted, was simply something that financial people were
inexperienced at after years of government regulations. This meant that there was a tendency
to lend money to risky businesses that were vulnerable to external shocks and perhaps also of
dubious potential productivity. Also, banks had a large willingness to take on considerable
risks. The reason for this might be the presence of what has been called “implicit guarantees”,
that is the banks assumed that the government would bail them out if they ran into trouble.
This “moral hazard” gave banks the incentive to engage in reckless lending, contributing to
the vulnerability of the situation. In addition there have been empirical studies performed in
Canada that show that competition among banks increases sharply following a deregulation of
markets33. This increased competition implies that “a typical bank extends financial services
more aggressively in the short run than it would in the long run”34. Banks simply try to
conquer parts of a newly open market as quickly as possible, expecting high revenues in the
future. Evidence in Thailand is consistent with this view. Official figures – probably
understating the true situation - suggest increased of credit extension in many traditonal high
risk sectors35. The appearence of 19 foreign banks who established themselves under the aegis
of the BIBF, further added to this process.
This lending fuelled a continuing expansion, and “expansion” often meant
acquisition of assets, especially real estate and land, which given inelastic supply meant a
sharp rise in the prices of such assets. From 1994 to 1996, fixed assets of the Thai companies
rose on average 30% per year36. This was also visible in increasing prices on the Thai stock
market. That these increases were in fact an asset bubble without stability became clear in
1996 when prices dropped suddenly - first in the real estate sector as supply exceeded
demand and tenants were becoming difficult to find. Since this led to troubles for highly debt-
levered firms the amount of non-performing loans increased. In March 1997 the size of non-
performing loans was officially 100 billion Bhats or 2.5 billion US dollars, but the true size of
“bad loans” has been estimated to three times this number.37 This fact became apparent later
as official figures set the percentage of non-performing loans to total outstanding loans at
33 Gruben et al.(1997) 34 ibid. 35 Ibid. 36 Alba(1999) 37 ibid.
24
47%38. It does seem however that the worst times for the banking sector came after the
currency crisis in June 1997, as the huge debt in foreign currency grew when the exchange
rate depreciated.
Finland39
Finland had considerable regulations of current account and financial markets before the early
80’s. Capital in- and outflows were restricted and in fact controlled by the central bank, and
interest rates were regulated at low levels. There were also considerable deposit requirements,
further slowing down the credit growth. Banks were granted tax exemptions which together
with a number of other factors - such as a small and inefficient securities market – implied
that firms to a great extent relied on the banking sector for financing. Gradually a process of
liberalization was undertaken. Restrictions on capital in- and outflows were loosened, causing
an increasing deficit in the current account as capital settled in Finland. Control over the
interest rates that banks charged was relaxed together with more lenient rules for deposits.
Banks were now freer to decide how much money to lend. While this process of liberalization
took place, there were no changes in supervision of banks and financial institutions, leaving
them a great deal of freedom to explore their new opportunities. Tighter supervision was not
introduced until 1991. For a number of years fairly unregulated banks thus operated within the
supervisional framework of regulated banks.
Following these liberalizations – which in effect were more or less completed in
1987 – the private sector started to borrow heavily. For years regulations had kept a large
demand for credit unsatisfied and now it was available in abundance. Previously the rationed
credit had been primarily allocated to the export sector. Now the sharp increase in borrowing
mainly went to households and producers for the domestic market. Households borrowed
above all in order to finance housing, while firms invested heavily. All this meant a boom in
the construction sector, a sharp increase in the pricing of land and real estate, and a large
increase in stock prices. With hindsight it is easy to see that the situation was not stable. “In
general, a bubble economy existed in Finland in the late 1980’s that was fuelled by a growing
debt burden.”40
38 Vassiliou(2001) 39 The following is based on Vihriälä(1999), and Anali et al(2002) 40 Anali et al.(2002)
25
In 1991 the fall of the Soviet Union – importer of 20% of Finnish export41 – and
lower demand from Western countries, led to a severe downfall in exports. This led to a
devaluation of the Finnish Markka by 12,6%, but it seems that this was more an attempt to
stimulate export rather than pressure due to capital outflows or a speculative attack. Since a
large share of the debt was financied by foreign capital, this devaluation hurt Finnish debtors
as their nominal loans grew. There was a surge in the number of bankrupt firms and non-
performing loans which hurt the banks that had financed them. This is visible in table 6.
Table 6: Number of bankrupcies
1987 1988 1989 1990 1991 1992 1993
Bankrupcies 2844 2583 2749 3634 6255 7391 6861
Source: Anali et al (2002)
The problems continued during 1992, probably undermining the Finnish capability to avoid
the currency crisis in September. Many of the largest banks were severely hurt, and had to be
bailed out by the government. There was an official comitment from the authorities to inject
up to 80 billion Markka into the banking sector if needed, which amounts to 15-20% of the
country’s GDP. In the end the final cost of the support to banks is estimated to have been 45-
55 billion Markkas. This opens up the question of “implicit guarantees”. Were there implicit
assumptions in the banking sector that the state would bail them out if they ran into trouble,
thus making them more risk-tolerant? Vihriälä (1997) finds that though the lending strategies
of the bank’s did not change a great deal during the lending boom, the fact that they
continuously pursued aggressive expansion during the last years of the bubble, suggests that
they were well aware of the risks. From the point of view of the investors in banking activity
he concludes: “As long as one is unwilling to accept the idea that investors in the banks’
uninsured liabilities did not understand the risks involved, at least not in 1989 and 1990, one
is forced to conclude that the investors must have anticipated that their claims on the banks
would be protected by the authorities.”
Mexico42
41 ibid. 42 The following is based on Desmet(2000) and Gruben et al(1997)
26
Following troubles in the early 80’s Mexico had nationalized its banks, and imposed strict
regulations, including interest rate ceilings and dictated allocation of credit. A process of
liberalization was begun in order to increase availability of capital to speed up development.
In a number of moves leading up to re-privatization of banks in 1991, the interest rate ceilings
were abandoned, reserve deposit requirements reduced and dictated allocation eliminated. The
effects were the usual ones: foreign capital inflow, and easier access to credit leading to a
lending boom in the private sector. Again, a great deal of inflow and and domestic credit
expansion was channeled into a debt-based growth in the private sector. The total debt of the
private sector increased from the equivalent to 20% of GDP in 1989 to 55.3% in 1994.43 It has
been suggested – on good grounds – that many of the projects financed by banks were in fact
credit risks which were accepted due to aggressive lending policies. Apart from the usual
claims of implicit guarantees, Gruben et al (1997) have performed a study which concludes
that hard competition among banks was also a contributor to this. Liberalization meant the
establishment of many new banks as well as a struggle to conquer parts of a mostly new
market previously locked by regulations. This meant that banks had a tendency to follow
more short-term strategies, thereby being more risk-tolerant. Also the inexperience of the
Mexican banking system in operating in a deregulated climate, where for years investment
decisions had been dictated by the authorities, led to poor selection of projects. In relation to
this, the Mexican accounting standards were quite insufficent, masking some of the
vulnerability of the situation.
As the competitiveness of the export sector worsened, and harder times hit
Mexico, the typical problems of a highly debt-leveraged economy appeared. Bankrupted firms
led to a high degree of non-performing loans – from roughly 0% of all loans in 1990 to 8% in
late 1994.44 Already in September 1994 the government had bailed out two banks and Desmet
(2000) suggests that the expectation that further bail-outs were coming might have triggered
the currency crisis in December through increasing expected inflation. The devaluation on 20
December – an adjustment according to authorities – further increased the growth of non-
performing loans as nominal debt in foreign currency grew. Two days later the Mexican Peso
was floated after massive capital outflows. As the currency depreciated these problems
became even worse. The government finally ended up bailing out several banks.
43 Desmet(2000) 44 Ibid.
27
Sweden45
In the first part of the 1980’s, Sweden - like many other countries at this time - deregulated
the financial and banking systems. This was after decades of tight regulation, with the central
bank giving directions for banking acitivites in weekly meetings with representatives of larger
banks. Lending and interest rate ceilings together with placement requirements – a portion of
bank assets had to be in the form of government bonds with long maturity and low interest
rates – led to a situation where “banks were, in effect, transformed into repositories for
illiquid bonds, crippled in their key function in screening and monitoring loans”.46 This meant
that the banking sector had very little experience in evaluating different projects, a fact that
might have led to bad decisions in a more liberal climate. Deregulations had been resisted for
a long time, yet when the process started it proceeded with surprisingly high speed. By 1985
ceilings and placement requirement had been lifted. The effects were immediate. During the
period between 1985 and 1990 total lending by domestic institutions increased 136%.
Meanwhile an asset price bubble was forming. This was probably not triggered by the large
credit expansion since the price increases before liberalization were bigger than after. But it
still seems that the price bubble was augmented by the banks’ investment policies: “Both
inexperience in a new environment and competition among credit institutions unleashed by
deregulation played important roles in this process.”47 The stock market index rose 118%
between 1985 and 1988, and the rate of price increase of commercial real estates in
Stockholm was the highest in Europe. The first signs of troubles emerged in 1989 as rents for
housing in Stockholm were so high that tenants could not easily be found. In a year the real
estate stock price fell by 25% and in 1990 it had fallen by 52%. The troubles in the real estate
sector together with a number of external shocks – including rising interest rates – revealed
the vulnerability in the banking sector. Banks and institutions that had invested heavily in real
estate now faced problems of non-performing loans and falling stock values. Credit losses
increased, from 1% av total lending in the end of 1990 to 3,5% in 1991 and 7,5% in 1992. In
1991 two of the six biggest banks, Första Sparbanken and Nordbanken, were insolvent. The
state injected capital into Nordbanken – being one of the major owners – and guaranteed
subsidized loans to Första Sparbanken. Before the end of the bank crisis, the state had paid
out 65 billion kronor or 4-5% of GDP to the banking sector.
45 The following is based on Andersson et al(2002) and Englund(1999) 46 Englund(1999) 47 Englund(1999)
28
Summary and comparison
These case studies of the banking sector show several similarities. First, all countries started
from a highly regulated financial system that was deregulated, causing a credit expansion.
This increased lending went mainly to the private sector that for years had been cut off from
satisfactory supply of capital. Different causes - including inexperience, implicit guarantees,
and aggressive competition among lenders – led to high debt leverage in risky enterprises.
With the appearance of some sort of shock, like the fall of the Soviet Union, the amounts of
non-performing loans started to cause troubles for the banks. Some of these similarities are
summarized in table 8.
Table 7: Similarities of compared countries Country Deregulation Rapid credit
expansion Price bubble Large amounts
of non-performing loans
Thailand Yes Yes Yes Yes Mexico Yes Yes No Yes Sweden Yes Yes Yes Yes Finland Yes Yes Yes Yes
The massive increase in credit extension is also clearly visible in the graphs below, depicting
total banking claims – that is the claims of all banking institutions including the central bank –
on the private sector.
29
Figure 5: Total banking claims on private sector, percentage of GDP48
So far similarities. What about differences that might explain the early response of investors
and speculators? Looking at these graphs suggests a quantitative difference: banking claims
on the private sector in Thailand were much larger than in the other countries. Reaching 160%
of GDP in 1997, the Thai figure is more than four times the Mexican. This extreme credit
extension might explain a great deal of the vulnerability that unfolded after 1996, and led to
such early responses of investors and speculators. Further vulnerability might have been
added by the large proportion of foreign investment in Thailand. If banks have a lot of foreign
liabilities, the withdrawal of these can put strain on the central bank’s reserves as the money is
exchanged into foreign currency . The graph below shows foreign liabilities as percentage of
total banking liabilities – that is foreign claims on banks divided by total claims on them - for
Thailand and Mexico (data is not available for Sweden and Finland). We see that there is a
considerable difference in the level of foreign liabilities.
48 Source: IFS (line 32c/line 99b)
30
Figure 6
Foreign claims on domstic banks relative to total claims on them
Source: Allegret et al (2003)
These differences, however, are a matter of degree not of kind. Comparing these four
countries clearly shows that the vulnerability of the Asian banking sector could have been
predicted. Furthermore, the graphs above suggest even higher potential vulnerability for
Thailand – higher debt leverage, higher proportion of foreign claims - meaning that the
situation would have been even more obvious. It is of course impossible to tell if the currency
crisis had occurred even without the banking crisis, but I think it is clear both from intuition
and observation that a banking crisis severely increases the risk of currency crisis.
4.3 External factors
The previous sections focused on factors inside the country: macroeconomic variables, and
the situation in the banking sector. Many aspects of these depend on government policy.
However there may be many possible causes of crisis that lie outside the country’s control,
such as the actions of and events in other countries, domestic “random” events, or the actions
of speculators. If such factors are a considerable cause of crisis, then the exact timing is
impossible to predict – even though vulnerability to them is not.
This section is a very brief analysis of different possible contributors of this
type. I here focus on policy changes performed by other countries, shocks or “random” events
31
that might cause capital flight, and the role of large speculators. Once more the countries
discussed are Thailand, Finland, Mexico, and Sweden. It should be noted that none of the
events discussed below could possibly cause a crisis in themselves. But together with
vulnerability visible in macroeconomic variables and/or the banking sector, they might well
become the igniting spark that sets off the fire.
Policy changes in other countries
The ERM-crisis in Europe in 1992, the Mexican in 1994-1995, and the Asian in 1997 all
share one common denominator: the countries’ exchange rates were to some extent pegged to
the currency of a country that prior to the crisis began to pursue a restrictive monetary policy.
In Europe the German unification 1989 led the government to follow a path of large fiscal
spendings.49 The effects of this were resisted by the German central bank which pursued
restrictive monetary policies. Interest rates were raised and capital started to flow to Germany
from other European countries. To maintain their pegs in the long run these countries would
have to raise their interest rates as well. But given domestic problems of varying degrees,
could the countries really afford to do so if the pressure kept growing? The speculators
presumely did not think so and launched their attacks. In the Swedish and Finnish cases the
problems in the banking sector might have strenghtened this suspicion.
In Mexico the peg (de facto) was against the US dollar.50 There were large
inflows of capital, much of it in dollar-denominated bonds with short maturity. In this
situation “unfortunately for Mexico, contractionary monetary policies began in the US in
February 1994, which were reciprocated in the other hard-currency money centers.”51 This
meant that the flow of foreign investment into Mexico “could not be sustained”52 at the same
time as the peso followed the dollar’s appreciation. Capital already in Mexico furthermore,
should have been more prone to slip away in order to benefit from high interest rates in these
hard-currencies centers.
This same contractionary policy in the US was to cause troubles for the Asian
countries. Most of these countries had pegs to the dollar and had copied the Thai strategy for
attracting large amounts of foreign capital, which in effect meant that it was very easy for
investors to convert local currencies for dollars. Thus the US monetary policy should not just
49 Saxena(1996) 50 Reinhart et al.(2002) 51 Allen(1999) 52 ibid.
32
have been a cause of the creeping real appreciation of Asian currencies, but also served as an
incentive to shift investments to the US.
All three of these crisis were thus in some way connected to restrictive monetary
policies in another country. This suggests a moral dilemma: is a country responsible for the
effects of its monetary policies in other parts of the world? Delving into this is of course not
possible here.
“Random” events
Here the cases of Sweden and Finland might not be of use as reference points. Throughout
this paper there has always been an assumption that emerging market countries are more
vulnerable than established ones. The assasinations of Swedish or Finnish opposition
politicians would probably not have triggered capital outflows as it did in Mexico. Investors
simply appear to have lower confidence in the stability of developing countries and their
ability to maintain a healthy economic climate when faced with such events. Therefore I will
not go into the Scandinavian experiences.
In the first quarter of 1994 the Mexican foreign reserves were close to 30 billion
dollars.53 The country was having some internal troubles manifested in the Chiapas uprising.
Investors were concerned but not panicked by this as is evident in the following internal
memo in a “major US bank”54: “While Chiapas, in our opinion, does not pose a fundamental
threat to Mexican political stability, it is perceived to be so by many in the investment
community.” Still as the rebels achieved a symbolic victory in January 1994 by occupying the
city of San Cristobal, there were no capital outflows. On the contrary capital continued to
pour in.55 But only two months later an opposition politician was assassinated. There were
considerable capital outflows, although it is impossible to know for sure that this was really
caused by the assassination. The fixed exchange rate was maintained without greater troubles,
but foreign reserves were nearly halfed in the process: from 29.3 billion dollars in February to
16.5 billions in June undermining future ability to maintain the peg. A period of calm
followed and reserves grew somewhat. Then another assassination of an opposition politician
occurred in October, followed by suggestions that the government itself was somehow
involved. In one month the reserves fell from 17. 667 billions in October to 12. 889 in
November. Dollar-denominated bonds worth around 17 billion dollars were to mature in
53 Gruben et al. (1997) 54 Allen(1999) 55 Gruben et al.(1997)
33
1995. If all investors wanted to get out of Mexico this meant that the peg would become
impossible to maintain.
Thailand on the other hand had for long been famous for being stable. But 1996
the country was rocked by a financial scandal that cast doubts over the entire banking sector.56
One of the banks – Bangkok Bank of Commerce – was suspected of fraud.57 Investigations
revealed that banking personnel had been using bank assets for their own ends. When the
Bank of Thailand took over the bank there were no immediate consequences. But this event
hurt confidence of investors in the way the central bank – being after all supervisor of
financial activities – was running things.58 From this moment investors and speculators were
probably more attentive to what was happening in the banking sector. This occurred
simultaneously with the bursting of the asset price bubble and other problems. Still it cannot
be ruled out that these acts of a few dishonest individuals in a highly sensitive environment
acted as a catalyst or even ignition of the crisis.
Large speculators
During the Asian crisis Malaysian prime minister Mahathir more or less put the entire blame
on the irresponsible and ruthless acts of one person: speculator George Soros: “All these
countries have spent forth years to build up their economies and a moron like Soros comes
along.”59 As political as this saying might well be, it still warrants some attention. Are large
speculators not just a mechanism of currency crisis, as in the theoretical models, but also a
causing factor in themselves? Or in more specified words: can the actions of large speculators
create herding effects, inciting a flood of capital outpour as investors panic? Although
speculators have been an important assumed mechanism in all theoretical generations, little
research has been invested into the role of large players. Following accusations like that of
Mahathir, the IMF performed a study that concluded that large speculators such as Soros –
following the analogy of predators attacking a prey - were “at the rear rather then the head of
the pack”.60 Still we know that Soros was involved in the ERM crisis, and that the asset value
of his Quantum Fund increased by 25% following the fall of the British pound. With this
background, is it really believable that minor speculators and investors did not react when the
56 Andersson et al (2002) 57 Vasililou(2001) 58 Andersson et al (2002) 59 Wang(1999) 60 Corsetti(2001)
34
Quantum Fund took a short position against the Thai baht in January 1997?61 If not, to what
extent might it have influenced the chances of a crisis occuring? The study of Corsetti et al
(2001) analyzes the contention that “the activity of large players in small markets...may
trigger crises that are not justified by fundamentals, destabilizing foreign exchange and other
asset markets”.62 Although avoiding to infer too strong a role for large players, it is here still
concluded that the IMF report’s contention is not supported by actual data. On the contrary
there is nothing to contradict the assumption that the hedging funds – such as Soros’ Quantum
Fund – were key players that were the first to move and that “their presence made other
investors more aggressive in their trading strategies.” Yet I cannot see that there is really
anything supporting this claim either, so the question is still open, and the literature on this
subject still in its “infancy”.63
While it is intuitive that large players should have an impact on the chances of a
crisis occurring, it seems unlikely that are the sole underlying cause. There has to be some
sort of vulnerability that both causes traders to act and makes the country more vulnerable to
them in a feedback process.
5. Conclusion and summary This paper analyzed the possibility to predict currency crises. To this end a number of
attempts were tried and/or evaluated. First a logit model was estimated, which was unable to
explain and predict crises. This indicates that just a few variables are not enough to predict a
crisis and that more complex approaches are both justified and needed. In the next sections, a
number of possible underlying causes were analyzed using a more descriptive and qualitative
approach. The aim here was to analyze if it had been possible to predict the Asian crisis in
1997 by looking at recent history. Thus similarities within a chosen setup of countries,
Thailand – representing Asia – Finland, Sweden and Mexico were sought. First
macroeconomic fundamentals from Finland, Mexico, Sweden, and Thailand did share some
similarities. More specifically, they all had substantial or growing current account deficits,
and had suffered a loss of competitiveness in the previous 5-year period. However the main
difference was the early respone of speculators and investors in Asia following visible
problems. This suggests that there was some underlying factor not captured by macroecomic
fundamentals. I also consider banking activities in the above mentioned countries. A similar
61 ibid. 62 Ibid. 63 ibid
35
pattern emerged in all four countries. Deregulations resulted in rapid credit extension, and a
banking sector exposed to risky businesses. Harder times hurt a lot of businesses, that
damaged the banks through large amounts of non-performing loans. This situation made the
countries vulnerable to currency crises. The similarites between the different countries
suggests that it would indeed have been possible to predict that Asian countries would
become vulnerable to currency crises through an overextended banking sector. Still there was
the difference in the time it took for investors and speculators to respond. Two important
factors were the larger extent of debt leverage and large proportion of foreign debt in Asian
countries. Although it is evident that each of the analyzed countries was vulnerable, the
question about what finally triggers a crisis remains unanswered. This led to a brief discussion
about shocks and events that might intreact with vulnerability and set off a crisis. The ultimate
effect of such factors remains an open question, but they cannot be ruled out as contributors to
crisis although the contention that they are causes of crisis in themselves – that is not
interacting with inherent vulnerability – can rather safely be dismissed.
So is it possible to predict a crisis? That depends on what we mean by “predict”.
It seems that a country’s vulnerability can indeed be detected. However, there is also the
mysterious question of the triggering of the crisis. Is this deterministic? Does the vulnerability
move closer and closer to a point in time where a crisis occurs, like a branch under increased
pressure that sooner or later snaps? Or is it instead the case that vulnerability exists and some
type of event sets off the crisis? The risk of a forest fire occurring is high in a season of
drought, but it is still the careless camper that sets it off. What is the careless camper in the
case of currency crises? I would suggest that future research separates the field in two areas:
one area focusing on vulnerability and the other on the triggering. At this point I think that
vulnerability – the drought season that makes the forest highly flammable – is possible to
sense, but that the triggering factor – the careless camper - is far more elusive. That means
that predicting a crisis in the sense of specifying an exact point in time is probably impossible.
All that we can do is detect vulnerability.
36
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Appendix
Table 8: Descriptive statistics Developed countries Developing countries Variable Crisis Non-crisis Crisis Non-crisis
CA/GDP -0, 0189 (0, 0385)
-0,00494 (0,0338)
-0,0380 (0,0469)
-0,0324 (0,0533)
BB/GDP -0, 166 (0,0447)
-0,0323 (0,0358)
-0,0534 (0,0928)
-0,0222 (0,0516)
GAP -0,00418 (0,0230)
0,000441 (0,0223)
0,00955 (0,0397)
0,00242 (0,0303)
∆REER -0,765 (17,137)
1,300 (12,290)
-0,768 (20,367)
-2,793 (17,942)
Table 9: Countries included in discrete-choice regressions
Developing countries Developed countries
Bolivia Australia
Brazil Austria
Colombia Belgium
Ecuador Canada
Indonesia Denmark
Korea Finland
Malaysia France
Mexico Italy
Morocco Norway
Pakistan Portugal
Paraguay Spain
Phillipines Sweden
Singapore Switzerland
Thailand
Venezuela
40
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