Developing an early warning system to predict currency crises in emerging markets

UNIVERSITY OF ECONOMICS

INSTITUTE OF SOCIAL STUDIES

HO CHI MINH CITY

THE HAGUE

VIETNAM

THE NETHERLANDS

VIETNAM – THE NETHERLANDS PROGRAMME FOR M.A IN
DEVELOPMENT ECONOMICS

DEVELOPING AN EARLY WARNING SYSTEM
TO PREDICT CURRENCY CRISES
IN EMERGING MARKETS

MASTER OF ARTS IN DEVELOPMENT ECONOMICS

By
HOANG THUY HONG NHUNG

Academic Supervisor
Assoc. Prof. NGUYEN VAN NGAI

Ho Chi Minh City, December 2014


CERTIFICATION

“I certify that the substance of this thesis has not already been submitted for any
degree and has not been currently submitted for any other degree.
I certify that to the best of my knowledge and help received in preparing this thesis
and all used sources have acknowledged in this dissertation”.

HOANG THUY HONG NHUNG
Date: 27th December 2014

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ACKNOWLEDGEMENTS
Upon completing this thesis, I have received a great deal of encouragement and
support from many people.
First of all, I would like to express my deep gratitude to Assoc. Prof. Nguyen Van
Ngai, my academic supervisor, for his patient guidance, enthusiasm and
encouragement..
I would also like to thank Dr. Truong Dang Thuy for his professional advices, and
Mr. Truong Hong Tuan and Mr. Luong Duy Quang, former students, for their
valuable comments.
My gratefulness is also extended to all of my lecturers and staff of the VietnamNetherlands Program, particularly, Assoc. Prof. Nguyen Trong Hoai and Dr. Pham
Khanh Nam for their assistance during the first days when I started this program.
I wish to thank my family for their encouragement and support during my study as
well. Without them, I would not have a chance to finish the thesis.
Finally, I would like to thank all my friends and other people who have had any
help and support for my thesis but are not above-mentioned.

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ABSTRACT
This thesis develops a new early warning system (EWS) model to predict the
currency crises in emerging markets by using the logit regression. According to the
results, the macroeconomic variables and the institution variables are valuable
indicators which play important roles in EWS model for predicting the currency
crises. It shows that the real exchange rate, export growth, import growth, current
account surplus/GDP, short-term debt/reserves have correct sign and are statistically
significant at 5% level. It also shows that the law and order, external conflict have
correct sign and are statistically significant at 1%. In addition, this thesis also
applies credit-scoring method to get the optimal cut-off threshold in order to have a
more accurate probability of predicting currency crises. Since then, the policymakers can consider taking the effective pre-emptive actions to prevent the currency
crises occurring in the future.

Key words: currency crisis, early warning system, emerging market, logit model

TABLE OF CONTENTS

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CHAPTER 1:

INTRODUCTION ........................................................................... 1

1.1. Problem statement .....................................................................................................1
1.2. Research objectives ...................................................................................................4
1.3. Research questions ....................................................................................................4
1.4. The scope of the thesis ..............................................................................................4
1.5. The structure of the thesis .........................................................................................5
CHAPTER 2:

LITERATURE REVIEWS.................................................................. 6

2.1. Definition of currency crisis ......................................................................................6
2.2. Theoretical literatures of currency crises ..................................................................7
2.2.1.

First generation models of currency crises ....................................................7

2.2.2.

Second generation currency crisis theoretical model.....................................9

2.2.3.

Third generation currency crisis theoretical model .....................................10

2.2.4.

“Fourth generation” currency crisis theoretical model...............................12

2.3. Empirical studies of currency crises ........................................................................14
2.3.1.

Indicators of currency crisis .........................................................................14

2.3.2.

Existing methods approach in EWS model of currency crisis ......................16

2.3.3.

Summary of recent empirical findings ..........................................................19

2.4. Conceptual framework ............................................................................................26
CHAPTER 3:

RESEARCH METHODOLOGY AND DATA................................... 28

3.1. The EWS model specification .................................................................................28
3.1.1.

Dating the currency crisis and define the dependent variable......................28

3.1.2.

Explanation variables choice and hypothesis testing ...................................29

3.1.3.

Methodology research ..................................................................................36

3.2. How to choose the optimal cut-off threshold ..........................................................39
3.3. Data collection.........................................................................................................42
3.4. Estimation strategy and statistical tests of the model ..............................................43
CHAPTER 4:

RESEARCH RESULTS ................................................................... 45

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4.1. The descriptive statistic of the sample ....................................................................45
4.2. Empirical results ......................................................................................................50
4.2.1.

Effected by macroeconomics factors ............................................................51

4.2.2.

Effected by institution factors .......................................................................53

4.3. Choosing the optimal cut-off threshold ...................................................................55
4.4. Predicting the currency crisis ..................................................................................58
4.4.1.

Asian Crisis 1997-1998 ................................................................................59

4.4.2.

Turkey crisis in 1994 and 2001.....................................................................59

4.5. Robustness test ........................................................................................................62
4.5.1.

Out-of-sample test of Latin America case.....................................................62

4.5.2.

Choosing optimal cut-off threshold of EWS model in Latin American.........64

4.6. Compare results with other empirical studies .........................................................65
CHAPTER 5:

CONCLUSIONS AND RECOMMENDATIONS .............................. 68

5.1. Main findings ..........................................................................................................68
5.2. Policy implications ..................................................................................................69
5.3. Research limitations ................................................................................................71
5.4. Suggestions for future researches ............................................................................72
REFERENCES

........................................................................................................ 73

APPENDIX A: LITERATURE WORKSHEET AND DATA SOURCES ....................... 77
APPENDIX B: RESULTS OF CHOOSING CUT-OFF THRESHOLDS AND
PREDICTING VALUE OF EWS MODEL IN ASIA..................................................... 90
APPENDIX C: RESULTS OF ROBUSTNESS TEST ................................................... 93
APPENDIX D: DISCRIPTIVE STATISTIC ................................................................. 96
APPENDIX E: COMPARISON OF TWO MODELS: MACROECONOMIC
VARIABLES ONLY AND INCLUDING INSTITUTIONS VARIABLES. .................. 108

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LIST OF FIGURES
Figure 2.1: The flowchart of developing an EWS model to predict currency crises ........... 19
Figure 2.2: Conceptual framework ..................................................................................... 27
Figure 3.1: Logit and probit cumulative distributions ........................................................ 37
Figure 3.2: The optimal cut-off identification ..................................................................... 42

Figure 3.3: Research processing ......................................................................................... 44
Figure 4.1: Optimal cut-off threshold of 12-months EWS model in Asian countries .......... 57

Figure B.1: The fitted and predicted value of EWS model in Asian countries .................... 90
Figure C.1: Optimal cut-off threshold of 12-months EWS model in Latin America ........... 93
Figure C.2: The fitted and predicted value of EWS model in Latin America countries ...... 94
Figure D.1: Reserve loss ..................................................................................................... 96
Figure D.2: Export growth ................................................................................................. 96
Figure D.3: Import growth .................................................................................................. 98
Figure D.4: Short-term debt/Reserves ................................................................................. 99
Figure D.5: GDP growth ................................................................................................... 100
Figure D.6: Current account/GDP .................................................................................... 101
Figure D.7: Real exchange rate growth ............................................................................ 102
Figure D.8: Government stability...................................................................................... 103
Figure D.9: Corruption ..................................................................................................... 104
Figure D.10: Law and order.............................................................................................. 105
Figure D.11: External conflict........................................................................................... 106
Figure D.12: Internal conflict ........................................................................................... 107

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LIST OF TABLES
Table 3.1: Summary expected sign of explanation variables .............................................. 36
Table 4.1: The summary of sample used in the regressions ................................................ 47
Table 4.2: The multicollinearity between independent variables ........................................ 48
Table 4.3: The correlation between independent variables ................................................ 49
Table 4.4: The empirical results of logit regression of 12-month EWS model .................... 51
Table 4.5: Specification error test ....................................................................................... 51
Table 4.6: Probability of predictability of 12-months EWS model (cut-off =13.27%) ....... 57

Table 4.7: EWS model performance with different cut-off point ......................................... 58
Table 4.8: Robustness test of Asian countries in 1994, 1997, 2001, 2007 .......................... 60
Table 4.9: Performance of EWS model in Asian countries when cut-off = 13.27% ........... 62
Table 4.10: The results of EWS model in Latin American countries ................................... 63
Table 4.11: Results of explanation variables compare with other empirical studies .......... 67

Table A.1: The summary references of explanatory variables of the model ....................... 77
Table A.2: Summary data, sources and period time of explanation variables .................... 79
Table A.3: The literature worksheets of empirical studies .................................................. 80
Table B.1: Identify optimal cut-off in Asian countries by Credit-scoring approach ........... 90
Table C.1: Probability of predictability of 12-months EWS model (cut-off =12.02%) ....... 93
Table C.2: Performance of EWS model in Latin American countries, cut-off = 12.02% ... 93
Table E.1: Comparing 12-month EWS predicting of 02 models in Asia: 1992 - 2011 ..... 109
Table E.2: Nested model test ............................................................................................. 110
Table E.3: Specification test of macroeconomic model ..................................................... 110
Table E.4: Specification test of full model ......................................................................... 110

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CHAPTER 1: INTRODUCTION
1.1.

Problem statement

There were a lot of financial crises which occurred during the 1990s: the crises of
European in 1992-1993, Mexico in 1994-1995, the crises of Asia in 1997-1998,
Brazil in 1999, Turkey in 2001, Argentina in 2002 and the economic crises over the
world in 2008-2009. These financial crises have strong influences on economy,
politics and society. They caused the economic uncertainty which suffered from

high inflation, slow growth, high unemployment and poverty. It made the GDP
growth rate is negative, the abrupt changes in nominal exchange rate with over 50%
devaluation. In Argentina, it lost 20% of GDP growth and the real wages decrease
match with it percentage. The policy-makers were all under the pressure of
implementing new policies in order to recover the affected economy. Moreover, the
cost of crises was very high, which led to an increase in the number of empirical
studies with the aim of constructing the monitoring tools to predict the crisis
occurrence. These studies were often called early warning system (EWS).
There are three common types of financial crises: currency crisis, banking crisis and
debt crisis. However, The EWS model in this thesis only focuses on the currency
crises like most of EWS models in previous empirical studies.
EWS models for currency crises were first built by Krugman (1979) and enhanced
by Flood and Garber (1984). They proved that reserve loss is an important indicator
to predict crises. Obstfeld (1994, 1996) has proposed a different model for
predicting currency crises. He stated that the currency crises occurred due to the
expectation of speculators. However, the model failed to take time matter into
account therefore it could not predict the time when crises occurred. After Asian
crisis in 1997, it has created the foundation to develop a new model for currency
crises. Kaminsky and Reinhart (1999) built the models of the EWS for twin crises
that combine banking crises and currency crises. They also stated that, banking

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crisis often occurred prior to currency crisis, when the currency crisis occurred, this
deepened the banking crisis; as the result the economy is in twin crises. In the
general, these studies used the macroeconomic and financial indicators to predict
the currency crises such as foreign reserves, export and import, real interest rate,
real exchange rate, M2/reserves, M2 multiplier, current account deficit (or surplus)
to GDP ratio, short-term debt/reserve (Kaminsky et al.,1998, Frankel and Rose,

1996, Berg and Pattilo, 1999). In the recent years, some economists concerned
about institutional factors such as bureaucratic quality, government stability,
government effectiveness, voice and accountability, rules of law, democracy,
election, control of corruption and so on (Block, 2003, Shimpalee and Breuer, 2006,
Leblang and Satyanath, 2008) that were used to predict the probability of imminent
crises.
Besides selecting the potential indicators, several methods have been suggested.
The most popular and suitable one is logit models that were applied by Frankel and
Rose (1996), Berg and Pattillo (1999). And the second is the signal approaches that
were proposed by Kaminsky et al. (1998) and applied by Edison (2003),
Bruggemann and Linne (2000), Subbaraman, Jones and Shiraishi (2003)). Some
alternative approaches are cross-country regression models which proposed by
Sachs et al. (1996), Ordinary least square (OLS) method such as Tornell (1999),
Brussiere and Mulder (1999), Markov-switching method applied by Martinez-Peria
(1999), Abiad (2003), and Artificial Neural networks (ANN) method applied by
Nag and Mitra (1999).
Nevertheless, most of the EWS models only focus on identifying the indicators,
which are statistically and economically significant, that should be included in the
models to predict the currency crises, the problem raised is that the ability to predict
of those EWS models were unexamined. In order to solve the problem, the optimal
cut-off threshold is chosen to evaluate the EWS model and minimizing the crisis
risk. If the chosen cut-off point is low, it will give more signals of crises, therefore

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more crises will be detected, however, resulting in false alarms (having the signals
but no crises happen – type 2 error) increase. Conversely, if the chosen cut-off point
is high, the fewer correct crises will be detected; thus, the missing signals (the crises
occur but no preceding alarm – type 1 error) will decrease. Kaminsky et al. (1998)

developed the method named the noise-signal-ratio (NSR) to choose the optimal
threshold that minimized the ratio of false signals to good signals. Berg and Pattilo
(1999) used the Quadratic probability score (QPS) and the Log probability score
(LPS) to prove that their EWS models have better forecasting ability than the
Kaminsky et al. (1998). Bussiere and Fratcher (2002) based on the Damirguc-Kunt
and Detragiache (1999) idea to build the loss function for policy-maker to predict
the currency crises. They said that the choice of optimal cut-off thresholds and the
predictive periods were based on the risk-aversion degree. In the recent years,
Candelon et al. (2012) who were the first ones to summarize many methods to
choose the absolute optimal cut-off points that highly contributes to evaluate the
EWS forecast performance. They concluded that Credit Scoring approach and
Accuracy measure are better than given cut-off point method or the noise-to-signal
ratio of Kaminsky et al. (1998).
Following the trend of this development and enhancement of EWS models, this
thesis will use seven macroeconomic variables that suggested by many previous
studies such as Kaminsky et al (1998), Berg and Pattillo (1999), Bussiere and
Fratzscher (2002) combine with five institutional indicators used by Shimpalee and
Breuer (2006); simultaneously, use the logit approach to develop EWS models in
terms of predicting the probability of currency crisis occurrence in emerging
markets. In order to evaluate the predictability of EWS models, this thesis will
apply the Credit-scoring approach according to Candelon et al. (2012). This is also
one of earlier paper studies applied Credit-scoring approach to evaluate the EWS
models performance to predict the currency crises.

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1.2.

Research objectives

Based on the crucial things mentioned above, some objectives of this thesis are to
be identified:
-

Identifying the crucial indicators that contribute to the EWS models to
predict the currency crises in emerging markets.

-

Identifying the optimal cut-off point to maximize the correctly prediction of
currency crises.

-

Suggesting some policy implications to prevent future currency crisis
occurrence.

1.3.

Research questions

To reach the research objectives, the following questions should be answered.
-

What are crucial indicators that will be used in the EWS models to forecast
the probability of currency crisis occurrence in emerging markets?

-

Which is the optimal cut-off threshold of the EWS models using to predict
the currency crises in emerging markets?

1.4.

The scope of the thesis

This thesis uses monthly data of 5 emerging markets in Asian area during the period
1992M1 – 2011M3, it included Indonesia, Malaysia, Philippines, Thailand and
Turkey. It focuses on the emerging markets because of two reasons. Firstly, several
paper studies on emerging markets have been done after the wave of financial crises
in the 1990s and succeeded in determining the indicators that lead to the currency
crises as well as constructing to the EWS models for preceding currency crisis
occurrence. Secondly, those countries are emerging markets, so it is reasonable to
assume that they have the same characteristics. The dataset started in January 1992

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because this thesis would include all the crises from 1990s, the dataset ended in
March 2011 because of the limitation of monthly economic data from IFS CDROM (2011).
1.5.

The structure of the thesis

The following sections of this thesis are organized as follow. Chapter 2 discusses
the literature review of the currency crises and the EWS models. Chapter 3 presents
the data and methodology researches. Chapter 4 describes the results obtained from
logit regression model, and credit-scoring approach, the robustness test and
comparison results with previous studies. And, Chapter 5 is the conclusion and

recommendations.

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CHAPTER 2: LITERATURE REVIEWS
This chapter presents three main parts. First of all, it introduces the definition of
currency crisis. Secondly, it presents the “four” theoretical generations which are
the theoretical literature of currency crisis. Thirdly, it summarizes existing empirical
researches, it includes (i) how to define currency crises, (ii) providing some existing
EWS models of currency crises, (iii) the indicators that were used in previous
studies, (iv) the findings of those researches. Finally, it presents the conceptual
framework.
2.1.

Definition of currency crisis

To build the EWS models, it is essential to identify the definition of a currency
crisis. In general, a currency crisis is defined as a situation in which speculative
attacks on the currency that leads to exchange rate depreciation or forces the
government to defend the currency depreciation by increasing the real interest rate
or selling the foreign reserves.
However, the currency crises need to be measured and transformed into the value.
Eichengreen, Rose, and Wyplosz (1995) made an important landmark of building a
method to measure the pressure of the currency and to date currency crises. They
enhanced the monetary model of Girton and Roper (1977) to construct the index of
exchange market pressure (EMP) that included the weight of nominal exchange
rate, foreign exchange and interest rate. The exchange rate is under pressure when
this index exceeds the certain threshold.
However, the method of Eichengreen et al. (1995) did not satisfy other researchers;

therefore, alternative methods were developed. The Frankel and Rose (1996)
dropped the foreign reserves and interest rate from EMP index to define the
currency crash index. They defined the currency crises occurring when the
depreciation of currency was greater than 25% and the rate of depreciation

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increased at least 10%. However, Berg and Pattillo (1999) argued that this
definition could lead to false positives because some countries were regularly
experiencing a fluctuation larger than 25% in exchange rate which were not be
problems when taking the 25% in depreciation.
Kaminsky et al. (1998, 1999) method was closed to the methods of Eichengreen et
al. (1995). They, however, dropped the interest rate because they argued that in
some countries of their samples, the interest rate would be controlled by the central
banks. Moreover, while Eichengreen et al. (1995, 1996) define the threshold when
EMP exceeds 1.5 standard deviations of mean, Kaminsky et al. (1998, 1999) used 3
standard deviations.
Bussiere and Fratzscher (2002) method was similar with Eichengreen et al. (1995),
except they used “real” variables. They take “the real exchange rate and the interest
rate are intended to account for differences in inflation rate across countries and
over time” (Bussiere and Fratzscher, 2002, p.9).
This thesis, thus, will follow the newest method which is based on Bussiere and
Fratzscher (2002) to date the monthly of currency crisis occurrence.
2.2.

Theoretical literatures of currency crises

Based on histological evidence of many currency crises occurred in the past, the
theoretical literatures of the currency crises are summarized in four generation

models as below:
2.2.1. First generation models of currency crises
The first-generation model was built by Krugman (1979) and enhanced by Flood
(1984) after the crises occurred during periods prior 1990s. Krugman stated that the
government could fixed the foreign exchange rate by several ways “it can use openmarket operations, intervention in the forward exchange market, and direct

7


operations in foreign assets to defend exchange parity” (Krugman, 1979, p.311).
Moreover, his model assumes that, there are two kinds of asset available: domestic
and foreign currency, then the only way to keep the exchange rate fixed is to sell
out the foreign reserve. Therefore, the government has to trade-off between two
cases: when they want to prevent the domestic currency depreciation, they have to
sell out the foreign reserves until it is exhausted; and when they want to prevent the
domestic currency appreciation, the government has to print money to increase
money supply, resulting in high inflation occurs. Then, when the economy is in high
inflation, it causes the composition of speculators’ asset portfolio change, the
proportion of foreign reserves will be increased and proportion of domestic reserves
will be decreased. In order to keep the pegged exchange rate, government has to sell
out it reserves on the way finance budget deficit. With such expectation, the
speculators sell domestic currency quickly and reserve run out faster. When the
reserves exhausted push government is not able to defend fixed exchange rate
anymore. When the government is no longer preventing the fixed foreign exchange
rate, the currency crises take place.
The first-generation models stated that the problems of balance of payment are the
main reasons of financial crises; it explained well the crises of Latin America
countries in 1980s that have the fundamental problems are macroeconomic such as
fiscal deficit, monetary excesses, and high inflation. Therefore, variables that are
usually used to precede the crisis in this period are international reserve loss, money

supply, international interest rate, budget deficit growth, current account deficit
growth, domestic credit growth and exchange rate growth.
On the other hand, there were many crises occur in European countries in 19921993 that could not be explained by the first-generation model. Although the
macroeconomics fundamental were believed to be applied in this area, crisis still
occurred. The currency crisis models were needed to be enhanced and developed to

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adapt with the current economic condition. Therefore, the second-generation
models were built up.
2.2.2. Second generation currency crisis theoretical model
The second generation models were built after crises in European countries in 19921993. According to Obstfeld (1994, 1996), the crises still occurred while European
countries have such the healthy macroeconomics fundamental; thus, it could not be
explained by Krugman’s model. Therefore, there were some other factors such as
the effect of high interest rate or unemployment growth that made the government
considered to response to the crises during 1992-1993. Then, once one doubt that
the government was willing to maintain the foreign exchange rate by borrowing
international reserves and used other policy options to deal with the crises and the
question was raised: “Why does government decide to abandon a pegged exchange
rate?”. He stated that there is the trade-off among variety of government’s policies.
In order to defend the currency devaluation, the policy-makers have to consider
between the cost and benefits, and when the costs exceed the benefits they are
willing to decide to abandon a pegged exchange rate target. Consequently, his
theory suggests that government policies are affected by the market’s expectations
and the expectations of the market are affected by the government’s policies.
Causality of both ways leads to the existence of multiple equilibriums.
The expectation of investors and speculators on whether the pegged foreign
exchange rate should be kept or not will have effect on government decisions.
Because when the people expected the domestic currency depreciates, the

expectations linking together reflect the pessimistic of the investors and the public
about the policy of government, the herd behavior of investors, they could convert
the domestic currency into the foreign currency. Consequently, their actions would
cause the depreciation of domestic currency. Due to multiple equilibriums, second
generation models explained crises as self-fulfilling outcomes.

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The second-generation models can explain well the European countries crises in
1992-1993. These crises did not come from weak macroeconomic fundamentals but
from self-fulfilling expectation of speculators and the existence multiple
equilibriums.
These models also suggest that any factor that is likely to influence the
government’s decision whether to maintain or abandon the peg exchange rate might
contain information on the likelihood of a crisis occurring. For the purpose of
econometric model, those factors have been constrained to the following variables:
level of unemployment, inflation, the amount and composition of debt and financial
sector stability.
However, the second-generation models could not detect the time of crisis
occurring. Moreover, the reality crises that happened in 1997-1998 led to develop
the new generation crisis model.
2.2.3. Third generation currency crisis theoretical model
The third generation models were built after the financial crises of Asian countries
in 1997-1998. According to Krugman (1999), the crises occurred because of moral
hazard and asset bubble. He stated that, Asian crises came from the moral hazard in
the financial system: the financial institutions who received the implicit guarantee
invested on the risky lending; moreover, they prefer to invest into highly risky
projects which are expected to generate higher returns even if with a very low
probability. The consequence of moral hazard and the risky investment is raising

asset prices. Moral hazard encourages financial institutions to increase investment
projects even low expected return. If the investment focuses on the supply of assets
fixed (such as real estate that most finance companies investing in Thailand), the
asset price will rise. An expectation- circle arises. People invest in real estate
because real estate prices expected to rise. When the amount of the investment
increases the demand, the prices are going up as expected. The bubble asset was

10


created. The financial institutions lend investors to invest in real estate are also
willing to continue lending as seen “too safe”.
Bubble price grows excessively until it booms when the investors realized that the
real situation or the real price was not reality, it was different from the expectation.
The reverse action occurred. They investors sell their assets because they expect the
price to decrease. The supply will then increase rapidly and become greater than the
demand, this excess supply will cause the price to fall; and as price falls the banks
realized the value of collateral for loans decrease and no longer lend for new loan,
also try to recover of old loans.
However, Radelet and Sachs (1998) said that the Asian crises occurred because
investors lost their confidence; they did not believe the foreign reserves were
enough for repaying the short-term debt. A bank is in normal trading and suddenly
depositors continuously make a substantial withdrawal of money that pushes the
bank into a difficult situation. At the beginning state, asset values remain greater
than the value of liability; it means the bank is still stable. However, when large
amount of money is withdrawn simultaneously, there will be insufficient cash flow
to cover the increasing demand. Thus, banks are no longer leaving the solvency (or
loss of liquidity). The bank claimed bank loans, refused to rollover and stopped to
new lending. The broker sold their stocks to exchange the foreign currency and
transferred money to foreign countries. From their perspective, the lack of

resolution mechanism of enterprise debt, bank debt forth both good and not good
enterprises have falling into trouble and depression the financial crises.
According to Kaminsky and Reinhart (1999), the models of this period are the early
warning system for “twin crises” that combine banking crises and currency crises.
They also stated that, bad loan from domestic debt, and short-term debts from
foreign bank are the causes of the banking crises and it often occurs to precede the
currency crises; when the currency crises occurring, this deepened the banking
crises; the result caused the economy being twin crises.

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Different from prior two generation models, the third generation models showed
that before the crises, Asian countries which have macroeconomic background are
relatively good as high GDP growth, low unemployment, inflation and government
spending be controlled, the budget deficit is low, the flow in of foreign investment
is large and political and economic are stable. However, behind the bright picture
are the problems of the banking system such as bad debt from borrowers in the
country with short-term foreign currency loans. The moral hazard relative with
strong political relation between government and finance institution, bank,
corporation (it called crony capitalism) when suggesting the implicit guarantees
from government. And, the bubbles of asset and stock prices were soaring and
plugging preceding the Asian crises.
Third generation models suggest some variables such as: real interest rate, lending
or deposit rate growth, domestic credit growth, M2/reserves, bank deposit, bank
cash/ bank asset, non-performing loan.
2.2.4. “Fourth generation” currency crisis theoretical model
Beside three generations models mentioned above, there seems to have additional
approaches to investigate the causes of the currency crises. Although, there were no
specific events that might attribute to this generation of currency crises, the

occurrences of currency crises following the 1997-98 Asian Financial Crisis such as
Russia (1998), Turkey (2000-2001), and Argentina (2001-2002) have rose up the
interested to find all possible causalities and linkages, which different from factors
already known in three theoretical models of currency crises, could lead to a
currency crises. Uncertainly, it may be called “fourth generation theoretical model”.
In these models, weak institutions worsen problems associated with economic
growth and contribute to causing currency crises.
According to Li and Inclan (2001), institutions affected currency crises in two ways.
First, institutions tend to have an impact and relative with the health of the national

12


economies. Then, the institutions can cause the bad economy and contribute to
currency crisis occurrence or institutions can create the good economic fundamental
and remove some reasons to cause the currency crisis occurrence. Second, the
institutions are the informative. The institutions sign to market agents about the
future economic fundamentals; thereby, it can shape market expectations. Then,
when institutions correlate with bad economies cause the instability market
expectation, increase the market uncertainty; cause more capital outflow and
increase the likelihood of currency crises. On the contrary, when institutions
correlate with good economies cause the stability market expectation, decrease the
market uncertainty, cause the capital outflow less and reduce the likelihood of
currency crises.
Acemoglu et al. (2002) stated that countries have poor macroeconomic policies also
have weak institutions, weak investor’s property rights, widespread corruption, and
a high degree of political instability. They implied that the poor macroeconomic
policies are the symptom of institutions problems rather than the instable economic;
it stated “weak institutions cause volatility through a number of microeconomic, as
well as macroeconomic channels”.

Leblang and Satyanath (2006) developed the framework model to link the
institution, speculation’s expectation and crises; in addition, the empirical proved
the better of their approach in terms of predicting. This study stated that, the divided
government and the government turnover increase the uncertain of speculator’s
belief and raise the likelihood of currency crisis. It showed that, the focal point for
the speculators’ predication of economic variables is the forecast of government.
However, speculators expectation will rely on their own predict if the government
announcement is not credible. Therefore, the new government needs time to
develop their accuracy forecast. During this time, the speculators deprived the
common of focal point. It implied that, following the government turnover is the

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period of uncertain of speculator’s belief and increase the probability of currency
crisis even the economic fundamentals is unchanged.
The fourth generation crisis models created more choice of variables in early
warning system for currency crises. Some variables suggested for these models are:
Prudential supervision, accounting and disclosures requirements, legal and judicial
systems, bureaucratic quality, government stability, absence of corruption, law and
order, absence of external conflict, election, absence of internal conflict, exchange
rate, capital control, absence of ethnic tensions, central bank independence, deposit
insurance, financial liberalization and legal origin.
2.3.

Empirical studies of currency crises

An EWS model included the specific definition of currency crisis and given the
structure to predict the likelihood of currency crisis occurrence. According to Glick
and Hutchison (2011), the contents of EWS model to predict the currency crises

typically require three parts as follows: (i) a method to define or date of the
currency crises as discussed in section 2.1, (ii) a set of explanation indicators, and
(iii) the statistical methods. This section will present some potential important
indicators that common used in the EWS models to predict the currency crises;
then, this section will present some methods that usually used in previous
researches, thereafter this thesis will summarize the findings of some empirical
studies.
2.3.1. Indicators of currency crisis
In fact, in order to EWS models operate effectively, the selections of indicators
using in the model are very crucial because they contribute to the accuracy
prediction of currency crisis occurrence.
The theoretical literature review and the previous empirical studies have identified a
large of potential variables regarding to currency crisis occurrence. Some

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representative studies such as Eichengreen et al. (1996), Frankel and Rose (1996),
Kaminsky et al. (1998), Kaminsky and Reinhart (1999), Berg and Patillo (1999),
Peltonen (2006), Shimpalee and Breuer (2006), Leblang and Satyanath (2008).
They identified many variables of macroeconomic fundamentals such as the growth
of real exchange rate, the growth of broad money, domestic credit growth, current
account surplus (or deficit)/ GDP ratio, reserve loss, export growth, import growth,
Short-term debt/reserve and institution factors such as government stability, control
of corruption, law and order, external conflict, internal conflict, voice and
accountability, regulation quality.
There are many variables that may possible to enter the predicting model for
currency crises. These indicators are classified into categories as below:


Capital account: M2/foreign reserves, foreign reserves growth, gross external
debt/ export and short-term debt/foreign reserves.



Current account: the growth of real exchange rate, export growth, terms of
trade, import growth, and current account/GDP,



Domestic and public real sector: public debt/ GDP, change in stock price, GDP
growth, an index of equity prices.



Financial sector: the growth of M1 and M2, M2 multiplier, domestic
credit/GDP, domestic real interest rate.



Institution factors: openness, exchange control, changes of government, degree
of political instability, legal or illegal executive transfer, and lack of control
corruption, external or internal conflict, voice and accountability, regulation
quality



Global economy: US interest rate, growth of world oil prices

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2.3.2. Existing methods approach in EWS model of currency crisis
During 1990s, there were many researchers such as Kaminsky et al. (1998)
Kaminsly and Reinhart (1999), Berg and Pattillo (1999), Frankel and Rose (1996),
Edison (2000), Bussiere and Fratzscher (2002) who pursued development of models
that have statistically and economically significant of predicting currency crisis
occurrence, known as “EWS model”. Two methods are common approached in
EWS model. There are the Signal approach (Kaminsky et al., 1998, 1999, Edison,
2003) and the Logit/Probit model (Frankel and Rose, 1996, Eichengreen et al.,
1995, Berg and Pattillo, 1999).
2.3.3.1. Signals Approach
The signals approach was first built by Kaminsy et al. (1998), they proposed many
indicators have the unusual exhibit prior the crises. In their researches, they selected
15 indicators from prior theoretical or empirical studies and they stated that
whenever each indicator exceeds the given threshold, it will issue the signal of
currency crisis. Therefore, choosing the optimal threshold is very crucial, because
when lower threshold, it will issue many signals and vice versa, when higher
threshold, many signal will not issue. When a signal is issued, it has two cases: (i)
the crisis occurs in following, implying that it is the good signal, (ii) the crisis does
not occur, indicating that it is the bad signal, or it called “false alarm”. When a
signal is not issued, it still has two cases: (i) the crisis does not occur, implying that
the signal was correct, (ii) the crisis occurs, implying that it is “missing alarm” of
crisis. It illustrates in matrix as below:

Signal
No Signal

Crisis occurs
A

Good signal
C
Missing signal

In which,

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Non-crisis occurs
B
False alarm
D
Bad signal


-

A: good signal - a number of months the indicators issue the signal, the crisis
occurs in following.

-

B: false alarm - a number of month the indicators the signal but the crisis does
not occur (Type 2 error) or called it “noise”

-

C: missing alarm - a number of month the indicators were not issued a signal
precede the crisis imminent (Type 1 error).

-

D: bad signal - a number of month the indicator “refrain” from the crisis occur,
it did not issue the signal and the crisis also does not occur.

She stated that, the predictors can observes only in A and D cell. It means, it will
issue the signal and the crisis will occur in following (within 24 months), thus A>0,
C=0. It will not issue the signal and the crisis will not occur in following (within 24
months), thus D>0, B=0.
They defined “optimal” threshold at minimize the noise-to-signal ratio (NSR), that
is the false signals to good signals ratio and calculate as equation below:
NSR = [B/(B+D)] / [(A/(A+C)]
Moreover, Kaminsky et al. (1998) suggested another way to interpret the signal
result of indicators by comparing the conditional probability of crisis [A/(A+B)]
with unconditional probability of crisis [(A+C/(A+B+C+D)] of the indicators from
above matrix. In order to the indicators have the good information; the conditional
probability has to higher than unconditional probability. In addition, in the purpose
early warning currency crisis in 24 months, their research also ranked indicators
according to their ability predict a crisis at the first signal.
Zhang (2001) said that this method is very useful. It is easy to applied and also easy
to interpret the problem regarding to the abnormal exhibit of each indicators.
Although it is the most basic application for EWS model to predict the currency
crisis, it has some shortcoming. Glick and Hutchison (2011) stated that due to

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