predicting indonesian currency crises using early … · the 1997/98 asian financial crisis was the...
TRANSCRIPT
PREDICTING INDONESIAN
CURRENCY CRISES USING EARLY
WARNING SYSTEM MODELS
by
Syaifullah
This thesis is presented for the degree of Doctoral of Philosophy
at The University of Western Australia
Economics Discipline
Business School
The University of Western Australia
2012
i
ABSTRACT
Following the collapse of the Bretton Woods system of exchange rate
management in the 1970s, the frequency of financial crises as well as the
number of countries involved tends to increase. Even today, financial crises are
still a major threat to many economies in the world and will undoubtedly
continue in the future. Indonesia is no exception. With an open economy,
Indonesia has experienced several financial crises. The 1997/98 Asian Financial
Crisis was the worst in recent decades. It affected not only Indonesian macro
economy but also the country’s social and political aspects. As a result this crisis
is known as a multi-dimension crisis.
The enormous impacts and huge recovery cost of financial crises encourage
policy makers and economists to find ways to prevent these crises. This study
aims to make a contribution in this field by constructing models to predict
financial crises, in particular for Indonesia. It adopts and extends the signal
model proposed by Kaminsky et al. (1998) as well as the discrete choice model
proposed by Eichengreen et al. (1996) and Frankel and Rose (1996). In addition,
as an alternative method, this study also applies the artificial neural network
(ANN) model.
The empirical findings indicate that these models perform well in predicting the
Indonesian currency crises within the 24-month crisis window; however, the
ANN model outperforms the other two models for both within and out of
samples. Furthermore, in terms of consistency, sensitivity and prediction power
of these models in predicting financial crises within three shorter crisis
windows, namely the 6, 12 and 18 month crisis windows, the ANN model is
also better than the other two models. Finally, the findings in this thesis support
the argument that financial crises may be predicted and hence preventative
measures may be implemented to deal with potential crises in the future.
ii
TABLE OF CONTENTS
ABSTRACT ……………………………………………………...……………… i
TABLE OF CONTENTS ……………………………………………………… ii
LIST OF TABLES …..…………………………………...……………………… vii
LIST OF FIGURES ……………………………………………………………... x
LIST OF ABRETIATIONS …………….……………………………………... xii
ACKNOWLEDGEMENTS ……………………………………………………. xiii
CHAPTER 1
INTRODUCTION
1.1. Background ……………………………………………..…………………… 1
1.2. Research Purposes and Contributions ………………………………..… 2
1.3. Outline of the Thesis ……………………………………..………………… 5
CHAPTER 2
LITERATURE ON THE EWS MODELS
2.1. Introduction ……………………………...………..………………………… 7
2.2. Theories and Models of Currency Crises …………………..………….…. 8
3.2.1. The First Generation Model …..…………………………………..… 10
3.2.2. The Second Generation Model …………………………….……….. 10
3.2.3. The Third Generation Model ……………………………………….. 12
2.3. The Currency Crisis Models and Predicting Crisis Models .………….. 15
2.4. Predicting Currency Crises and the Role of EWS Models ….…………... 18
2.5. Conclusion …………………...………………………………………………. 23
iii
CHAPTER 3
THE INDONESIAN CURRENCY CRISIS EPISODES
3.1. Introduction ……………………………..…………………...……………… 25
3.2. Reversal of Fortunes: From Miracles to Crises ...………………………... 26
3.2.1. Prior to Crisis ……………………………………..…………………. 26
3.2.2. Indonesia in Crisis ……………………………….…………………. 27
3.2.3. The Impacts of the 1997/98 Asian Financial Crisis …………….. 29
3.3. Identifying the Causes of the 1997/98 Asian Financial Crisis ………….. 31
3.3.1. External Factors: Financial Contagion …………..………………… 31
3.3.2. Internal Factors: the Weakening of Domestic Economic
Fundamentals ……………………………..………………………....
32
3.4. After the 1997/98 Asian Financial Crisis ………………………………… 35
3.4.1. The Long Road to Economic Recovery ….………..………………. 35
3.4.2. The 2008/09 Global Financial Crisis ……………………………… 39
3.4.3. The Comparison between the 1997/98 Asian Financial Crisis
and the 2008/09 Global Financial Crisis …..………………………
41
3.5 Defining a Currency Crisis ……………………………………………….... 44
3.6. Conclusion …………………………………………………………………… 47
CHAPTER 4
PREDICTING INDONESIAN CURRENCY CRISES USING
THE SIGNAL MODEL
4.1. Introduction …………...…………………………………………………… 49
4.2. A Survey of Empirical Signal EWS Models ……………………………… 49
4.3. Methodology ………………………………………………………………… 52
4.3.1. Selecting Leading Indicators …………………………...………….. 53
4.3.2. Constructing a Composite Index ………..………………….……… 56
4.3.3. Generating the Probability of a Currency Crisis ………………… 57
4.3.5. Model Performance Evaluation …………………………………… 57
iv
4.4. The Application of General Signal EWS Model for Predicting
Indonesian Currency Crises ………………………………………………
60
4.4.1. Constructing the Signal EWS Model …………………………….. 60
4.4.2. Predicting Indonesian Currency Crises ………………………….. 64
4.4.3. The Signal Model’s Performance Evaluation ………..…………… 69
4.5. Assessing Sector Specific Forecasting and the Crisis Channels ……… 71
4.5.1. Capital Account Sector Specific Signal EWS Model ……………. 71
4.5.2. Current Account Sector Specific Signal EWS Model …………… 73
4.5.3. Financial Sector Specific Signal EWS Model …………………….. 75
4.5.4. Fiscal Account Sector Specific Signal EWS Model ………………. 76
4.5.5. Global Economy Sector Specific Signal EWS Model ……………. 77
4.5.6. Real Sector Specific Signal EWS Model ………………………….... 78
4.5.7. Performance Evaluation for Sector Specific Forecasting Results .. 79
4.6. Conclusions ………………………………...………………………………... 81
CHAPTER 5
PREDICTING INDONESIAN CURRENCY CRISES USING
THE DISCRETE CHOICE MODEL
5.1. Introduction …………………………………………………………………. 87
5.2. Literature Review ……………………………………..…………………….. 88
5.3. The Discrete Choice Probit/Logit Model ………………..………………. 92
5.3.1. The Probit Model ………………………………………….………… 93
5.4. The Application of the Probit/Logit EWS Model for Predicting
Indonesian Currency Crises …………………………………..……………
94
5.4.1. Constructing the Probit/Logit EWS Model ………………….…… 94
5.4.2. Estimation Results …………………………………..……………… 99
5.4.3. Predicting Indonesian Currency Crises …………….……………. 102
5.4.4. The Probit EWS Model’s Performance Evaluation ……………… 107
5.5. Conclusions ….………….………………………..………………………… 110
v
CHAPTER 6
PREDICTING INDONESIA CURRENCY CRISES USING
THE ARTIFICIAL NEURAL NETWORK MODEL
6.1. Introduction ………………………...………………………..……………… 112
6.2. Literature Review on the Application of the ANN EWS Model ……… 112
6.3. Specification of the ANN Model …………………………………..……… 117
6.3.1. Architecture of the ANN Model …………………………………… 117
6.3.2. The Learning Algorithm …………………………………………… 120
6.4. The Application of the ANN EWS Model for Predicting Indonesian
Currency Crises ………………………………..………………………….…
125
6.4.1. Constructing the ANN Model ………………………..................... 125
6.4.2. Predicting Indonesian Currency Crises ………………………….. 135
6.4.3. The ANN EWS Model’s Performance Evaluation ……………… 139
6.5. Conclusions ………………….……………………………………………... 142
CHAPTER 7
EARLY WARNING SYSTEM MODELS: COMPARISON
AND CONSISTENCY
7.1. Introduction ……………..……………………………………………...…… 151
7.2. Modelling Results Using a 24-Month Crisis Window ...……………….. 152
7.2.1. In-Sample Prediction Using a 24-month Crisis Window ……….. 152
7.2.2. Out-of-Sample Prediction Using a 24-month Crisis Window...…. 155
7.3. The Sensitivity Tests for Shorter Crisis Windows …………………....... 159
7.3.1. The Signal Model ….…………….………………………………….. 160
7.3.2. The Probit Model ……………………………..……………………... 179
7.3.3. The Artificial Neural Network Model ……..……...……………… 187
7.4. Model Comparison Using a Shorter Crisis Window ……………………. 198
7.4.1. In-Sample Prediction Using a 12-month Crisis Window .……….. 199
7.4.2. Out-of-Sample Prediction Using a 12-month Crisis Window ….. 202
7.5. Conclusions …………….…………..……………………………………….. 207
vi
CHAPTER 8
CONCLUSIONS
8.1. The Main Findings ……………………..…….………..…………...………. 219
8.2. Directions for Future Research ………………………………………….…. 221
BIBLIOGRAPHY ……………………………..………………….……………… 222
vii
LIST OF TABLE
TABLE 3.1 GDP Growth of Asia-5 (% per annum) ………………………... 27
TABLE 3.2 Moody’s and Standard and Poor’s Long Term Debt Ratings for Indonesia Prior to Asian Financial Crisis, 1996-1997 …….
27
TABLE 3.3 GDP Growth by Sectors, 1996-2007 ……………………………. 38
TABLE 3.4 The Difference between the 1997 Asian Financial Crisis and the 2008/09 Global Financial Crisis on Indonesian Economy..
43
TABLE 3.5 The Indonesian Crises Episodes Based on Previous Studies… 47
TABLE 4.1 Performance Matrix of Early Warning Indicator …………….. 55
TABLE 4.2 The Performance Evaluation of Individual Indicators ………. 61
TABLE 4.3 Composite Index and Probabilities of a Currency Crisis ……. 64
TABLE 4.4 The General Signal Model’s Performance Evaluations …….… 70
TABLE 4.5 Performance Evaluation of the Sector Specific Signal EWS Models …………………………………………………………….
80
TABLE 4.6 The Forecasting Results on Indonesian Currency Crises, 1970-2008 ………………………………………………………………..
81
TABLE A4.1 The List of Leading Indicators ………………………………….. 82
TABLE A4.2 The List of Leading Indicators for the Capital Account …….. 85
TABLE A4.3 The List of Leading Indicators for the Current Account ……. 85
TABLE A4.4 The List of Leading Indicators for the Financial Sector ……… 85
TABLE A4.5 The List of Leading Indicators for the Fiscal Account ………. 86
TABLE A4.6 The List of Leading Indicators for the Global Economy …….. 86
TABLE A4.7 The List of Leading Indicators for the Real Sector …………… 86
TABLE 5.1 List of Explanatory Variables …………………………………… 97
TABLE 5.2 The Descriptive Statistics ............................................................... 98
TABLE 5.3 The Correlation Matrix ………………………………………….. 99
TABLE 5.4 The General Model (Model 1)’s Regression Results …………. 100
TABLE 5.5 The Specific Model (Model 2)’s Regression Results …………. 102
TABLE 5.6 Determinants of Indonesian Currency Crises ………………… 102
viii
TABLE 5.7 The Probit Model’s Performance Evaluation …………..….….. 109
TABLE 6.1 List of Input Nodes for Model 1 ……………………………..… 127
TABLE 6.2 List of Input Nodes for Model 2 ……………………………..… 128
TABLE 6.3 Elements of Artificial Neural Network Architecture ……..… 133
TABLE 6.4 Average Contribution of Input Nodes to Output Node for Model 1 ………………………………………………………….…
134
TABLE 6.5 Average Contribution of Input Nodes to Output Node for Model 2 …………………………………………………….………
134
TABLE 6.6 The ANN Model’s Performance Evaluation ………..………… 141
TABLE A6.1 The Weights and Adjustment Weight from Input to Hidden Layers for Model 1 …………………………………..……………
144
TABLE A6.2 The Weights and Adjustment Weight from Hidden to Output Layers for Model 1 ………………………………………
147
TABLE A6.3 The Weights and Adjustment Weight from Input to Hidden Layers for Model 2 …………………………….…………………
148
TABLE A6.4 The Weights and Adjustment Weight from Hidden to Output Layers for Model 2 ………………………………………
150
TABLE 7.1 In-sample Evaluation Using a 24-month Crisis Window ……. 155
TABLE 7.2 Out-of-Sample Evaluation Using a 24-month Crisis Window 158
TABLE 7.3 The Signal Model with Fixed NSR’s In-Sample Evaluation …. 163
TABLE 7.4 The Signal Model with Fixed NSR:’s Out-of-Sample Evaluation …………………………………………...…………….
167
TABLE 7.5 List Indicators Based on NSR for Various Crisis Windows …. 169
TABLE 7.6 The Signal Model with Adjusted NSR’s In-Sample Evaluation …………………………………………………………
174
TABLE 7.7 The Signal Model with Adjusted NSR’s Out-of-Sample Evaluation …………………………………………………………
178
TABLE 7.8 The Probit Model’s Regression Results for Various Crisis Windows …………………………………………………………..
180
TABLE 7.9 Determinants of Indonesian Currency Crises ………………… 181
TABLE 7.10 The Probit Model’s In-Sample Evaluation ……………………. 184
TABLE 7.11 The Probit Model’s Out-of-sample Evaluation ………………. 188
TABLE 7.12 The Training Parameter for ANN Models ……………………. 189
ix
TABLE 7.13 Average Contribution of Input Nodes to Output Node …….. 190
TABLE 7.14 The ANN Model’s In-Sample Evaluation ……………….……. 193
TABLE 7.15 The ANN Model’s Out-of-sample Evaluation ……………….. 197
TABLE 7.16 In-Sample Evaluation Using a 12-month Crisis Window …… 201
TABLE 7.17 Out-of-sample Evaluation Using a 12-month Crisis Window.. 205
TABLE A7.1 In-Sample Evaluation Using a 6-month Crisis Window …….. 212
TABLE A7.2 Out-of-Sample Evaluation Using a 6-month Crisis Window .. 214
TABLE A7.3 In-Sample Evaluation Using a 18-month Crisis Window …… 216
TABLE A7.4 Out-of-Sample Evaluation Using a 18-month Crisis Window 218
x
LIST OF FIGURE
FIGURE 3.1 The Development of Banks in Indonesia, 1988-1999 ………. 33
FIGURE 3.2 Indonesian GDP Growth, 1991-2007 …………………………. 37
FIGURE 3.3 Indonesian Social Indicators, 1999-2008 ……………………... 39
FIGURE 3.4 EMPI, Thresholds, and Currency Crisis Episodes …………. 46
FIGURE 4.1 Composite Index, 1970-2008 ...………………………….……... 63
FIGURE 4.2 The General Signal Model: In-Sample Prediction ………….. 66
FIGURE 4.3 The General Signal Model: Out-of-Sample Prediction …….. 67
FIGURE 4.4 Probability of a Crisis for Capital Account, 1970-2008 …….. 72
FIGURE 4.5 Probability of a Crisis of Current Account, 1970-2008 …….. 74
FIGURE 4.6 Probability of a Crisis for Financial Sector, 1970-2008 …….. 75
FIGURE 4.7 Probability of a Crisis for Fiscal Accounts, 1970-2008 ……... 77
FIGURE 4.8 Probability of a Crisis for Global Economy, 1970-2008 ……. 78
FIGURE 5.1 The Probit Models: In-sample Prediction …………..……….. 104
FIGURE 5.2 The Probit Models: Out-of-sample Prediction ……..……….. 105
FIGURE 6.1 Architecture of the ANN Model ……………………………… 118
FIGURE 6.2 A Single Hidden Neuron ……………………………………… 120
FIGURE 6.3 Numbers of Hidden Neurons vs. RMS Errors ………………. 131
FIGURE 6.4 The ANN Models: In-Sample Prediction …………..………… 136
FIGURE 6.5 The ANN Models: Out-of-Sample Prediction …………..…… 138
FIGURE 7.1 In-Sample Prediction Using a 24-month Crisis Window ….. 153
FIGURE 7.2 Out-of-Sample Prediction Using a 24-month Crisis Window..........................................................................................
156
FIGURE 7.3 The Signal Model with Fixed NSR’s In-Sample Prediction… 162
FIGURE 7.4 The Signal Model with Fixed NSR’s Out-of-Sample Prediction ………………………………………………………..
165
xi
FIGURE 7.5 The Signal Model with Adjusted NSR’s In-Sample Prediction ………………………………………………………..
172
FIGURE 7.6 The Signal Model with Adjusted NSR’s Out-of-Sample Prediction …………………………….……………………….…
175
FIGURE 7.7 The Probit Model’s In-Sample Prediction ………………….... 183
FIGURE 7.8 The Probit Model’s Out-of-Sample Prediction ……………… 186
FIGURE 7.9 The ANN model’s In-Sample Prediction …………………….. 192
FIGURE 7.10 The ANN model’s Out-of-Sample Prediction ……………….. 194
FIGURE 7.11 In-Sample Prediction Using a 12-month Crisis Window ….. 200
FIGURE 7.12 Out-of-sample Prediction Using a 12-month Crisis Window 203
FIGURE 7.13 In-Sample Comparison Using Various Crisis Windows and Cut-off Probabilities, 1970/71-1995 …………………………..
210
FIGURE 7.14 Out-of-sample Comparison Using Various Crisis Windows and Cut-off Probabilities, 1996-2008 …………………..………
210
FIGURE A7.1 In-Sample Prediction Using a 6-month Crisis Window…...… 211
FIGURE A7.2 Out-of-Sample Prediction Using a 6-month Crisis Window... 213
FIGURE A7.3 In-Sample Prediction Using a 18-month Crisis Window……. 215
FIGURE A7.4 Out-of-Sample Prediction Using a 18-month Crisis Window 217
xii
LIST OF ABBREVIATION
ADB : Asian Development Bank
AFC : Asian Financial Crisis
ANN : Artificial Neural Network
ASEAN : Association of South East Asian Nation
BAPPENAS : Badan Perencanaan Pembangunan Nasional (National
Development Planning Agency)
BI : Bank Indonesia
BPNN : Back-Propagation Neural Network
CDS : Credit Default Swap
CI : Composite Index
EMS : European Monetary System
EWS : Early Warning System
EMPI : Exchange Market Pressure Index
ERM : European Regional Monetary
GDP : Gross Domestic Production
GFC : Global Financial Crisis
GRNN : General Regression Neural Network
GSB : Global Score Bias
HPAEs : High-Performing East Asian Economies
IBRA : Indonesian Bank Restructuring Agency
IMC : Intrinsic Mode Components
IMF : International Monetary Fund
INDRA : Indonesian Debt Restructuring Agency
JITF : Jakarta Initiative Task Force
LDA : Linear Discriminant Analysis
LM : Lavenberg Marquardt
Malari : Malapetaka Lima Belas Januari in 1974 (15 January 1974’s
disaster)
MDG : Millennium Development Goals
xiii
MPR : Majelis Permusyawaratan Rakyat (People’s Consultative
Assembly)
NIEs : New Industrialized Economies
NSR : Noise-to-Signal ratio
Pakfeb : Paket Kebijakan February 1991 (Government Deregulation
Package in February 1991)
Pakjun : Paket Kebijakan Juni 1983 (Government Deregulation Package in
June 1983)
Pakto : Paket Kebijakan Oktober 1986 (Government Deregulation
Package in October 1986)
Pr* : Cut-off-Probability
QDA : Quadratic Discriminant Analysis
QPS : Quadratic Probability Score
Rp : Rupiah
SBI : Sertifikat Bank Indonesia (Bank Indonesia Certificate)
USA : United States of America
yoy : Year-on-year
xiv
ACKNOWLEDGEMENTS
Finally, after all those years, I can finish my thesis. I realize that this thesis
would not have been completed without the support and help form numerous
people and parties. First and foremost, my gratitude to my coordinating
supervisor, Professor Yanrui Wu and my co-supervisor, Professor Nicolaas
Groenewold, who have guided, supported and encouraged me throughout my
PhD tenure in the University of Western Australia.
I also acknowledge the support of Dr Anggito Abimanyu, Dr Irfa Ampri, Yayan
and Dalim (Indonesian Fiscal Policy Office, Ministry of Finance), Dr Brad
Armstrong (Australian Treasury) for encouraging me to continue my study and
Australian Development Agency (AusAid) for providing me a scholarship,
namely the Australian Leadership Award (ALA).
I thank my Indonesian friends (Revalin, Hartono, Wahyu, Gugup, and Dekar),
and my fellow PhD students at Economics Discipline in UWA Business School
for all interesting discussions and the fun we have had during my stay in Perth.,
I would also like to thank Ms Deborah Pyatt and all administration staffs in
Economics Discipline for their supports during my research and Mr Mel Davies
for helping editing the final draft.
Finally, I would like to thank my parents, my wife (Laina) and my children
(Farhan and Naya) who have cherished me with every great moment and
always supported me whenever I needed it. Last but not the least; I would like
to thank the one and only, ALLAH SWT, for giving me the strength and health
and answering my pray to finish my thesis.
Perth, January 2012
1
CHAPTER 1
INTRODUCTION
1.1. Background
Since the 1970s, the number of countries hit by crises has tended to increase.
Examples include the Latin American crises of the 1970s/80s, the EMS crisis in
1992/93, the Mexican crisis in 1994, the Asian Financial crisis in 1997/98, Russia in
1998, Brazil in 1999, Turkey in 2000, Argentina in 2002, Uruguay in 2002, USA in
2007, and the global financial crisis in 2008. Indonesia is no exception. The country
has an open economy and has experienced several financial crises, with the Asian
Financial Crisis of 1997/98 being the most severe to hit the country. Even today,
currency crises are a threat for many countries around the world and will
undoubtedly continue to occur into the future.
The costs associated with these crises are huge. According to Hutchison and Noy
(2002), currency crises can lead to average growth reductions of between 5-8%,
while the decline due to banking crises is on average about 8-10%. Other works
that examine emerging economies have claimed the impact of currency crises can
reduce output growth by 2-3%. Laeven and Valencia (2008) indicated the average
fiscal cost associated with resolving a financial crisis was about 16% of GDP. The
cost of a currency crisis occurring simultaneously with a banking crisis or twin
crises (Kaminsky and Reinhart, 1999), is obviously enormous and can have a
devastating effect upon an economy. In addition to financial chaos and
disruptions, the impact also includes adverse social effects such as increasing the
numbers forced into poverty and unemployment.
In Indonesia the 1997/98 Asian Financial Crisis was such a multidimensional crisis
that it had serious social and political ramifications. Economically, the crisis caused
a decline in economic growth by 13.1%; gross fiscal costs were high and output loss
2
related to banking distress reached 56.8% and 67.9% of GDP (Laeven and Valencia,
2008). The social costs of the crisis were also tremendous, for after experiencing a
remarkable poverty drop prior to the crisis, poverty in Indonesia was estimated to
have risen from 17.72% in 1996 to about 24.2% of the whole population in the wake
of the crisis in 1998 (Balisacan et al., 2002). In absolute terms, this saw
unemployment increase by about 14 million, from about 6 million at the beginning
of the crisis to 20 million at the end. The impact engendered both social and
political disorder, culminating in the Jakarta riots that were followed by Suharto’s
resignation as the President of Indonesia on 21 May 1998.
The severity of these events has motivated many studies of the causes of past
currency crises in an attempt to identify common underlying factors. Such research
it is hoped will help prevent the occurrence of future crises. One of these efforts is
to develop econometric models for detecting a country’s vulnerability to crises.
1.2. Research Purposes and Contributions
In the aftermath of the 1997/98 Asian Financial Crisis, there has been an increasing
desire among scholars, policy makers, and international organizations, to develop
econometric models that can predict currency crises. Thus, the emphasis has been
on developing early warning system (EWS) models. Broadly speaking, there are
many forecasting techniques and methods that are available, but the most widely
used are the signal or indicator approach pioneered by Kaminsky et al. (1998), and
the probit/logit model proposed by Eichengreen et al. (1996) and Frankel and Rose
(1996). However, the results of these models in predicting currency crises are
mixed (Goldstein et al., 2000, Berg and Pattillo, 1999a, Kaminsky and Reinhart,
1998, Edison, 2000, Peltonen, 2006). A major problem in these previous studies is
that they have mainly focused on cross-country or regional analyse that assume all
countries are the same. This means they cannot capture specific characteristics for
each country, which limits their powers of prediction. The objective of this study is
3
therefore to develop EWS models that can predict Indonesian currency crises.
Towards this goal, this study employs two widely used EWS models, namely the
signal approach and probit/logit model, plus one alternative EWS model, the
artificial neural network (ANN) model. Because of the poor predictive capacity of
these standard EWS models in regards to Indonesia, an attempt will be made to
strengthen their prediction power so that they can be used as EWS models to
predict Indonesian currency crises.
In reinvigorating the signal approach, this study analyses only Indonesia, which
allows for greater flexibility, as the number of indicators can be increased
significantly. Subsequently, a set of 55 monthly leading indicators from 1970 to
2008 that can be divided into 6 groups will be utilised. These groups represent the
capital account, the current account, the financial sector, the fiscal sector, the global
economy and the real sector. However, as a non-parametric approach, the signal
approach is unable to define the source of a crisis. To deal with this issue, and
taking advantage of the large number of indicators, a sector specific signal
approach will be developed so as to define the sources and subsequent channelling
of currency crises in Indonesia.
Unlike the signal approach, the probit/logit model cannot employ a large set of
explanatory variables due to the problem of multicolinearity (Zhuang and
Dowling, 2002). This study selects the set of explanatory variables by employing
the noise-to-signal approach. This is commonly used in signal approach research to
evaluate and select the leading indicators by comparing the ability of each
indicator sending more good signals while simultaneously eschewing bad signals.
By using the top ten leading indicators based on this method, this study will
attempt to improve the predictive power of this model to trace Indonesian
currency crises.
The ANN model will also be used as an alternative EWS model, following its
previous success in predictions in other fields, and in order to take up the
suggestion by Edison (2000) and Kaminsky et al. (1998) that new techniques or
4
methods should be applied. Kamruzzaman et al. (2006) found that the ANN model
was better than multiple regressions for real life problem solving, including those
problems associated with finance and manufacturing. Furthermore, based on their
survey of 72 papers applying and comparing ANN and logistic models, Dreiseitl
and Ohno-Machado (2002) found that the ANN model generally outperformed the
logistic model because it had several features that were not available in the
standard models. These features include fault tolerance, generalization, and
adaptability (Medsker et al., 1996).
The ANN model can also be trained to improve its predictions; however, the
quality of the data set is also important here (Walczak and Cerpa, 1999).
Additionally, due to their similarities, this model can also be used as an alternative
to logistic regressions and many studies can be quoted (Tu, 1996, Ottenbacher et
al., 2004, Dreiseitl and Ohno-Machado, 2002). Thus, in order to improve the
predictive performance of this model and also to make it more comparable with
the probit/logit model, this study will select a set of input neurons using the top
ranked indicators that based on the noise-to-signal ratio.
The sensitivity and consistency of these three EWS models will also be tested by
shortening the crisis windows to 6, 12 and 18 month periods, and to define the best
EWS model for predicting Indonesian currency crises, the performance of these
three EWS models across prediction horizons will be compared. Finally, this study
extends the sample periods to 2008, so as to see whether the respective models are
able to capture any periods of vulnerability following the Asian Financial Crisis of
1997/98.
By embracing the above EWS models, utilising 55 monthly leading indicators,
adopting sensitivity and consistency tests, and comparing the ability of these
models over periods of 6, 12, 18 and 24 month windows, it is hoped that predictive
tools can be discovered that will enable periods of currency crises to be identified
in the future, and which might therefore prove valuable in allowing evasive
actions to be taken, especially within Indonesia.
5
1.3. Outline of the Thesis
The next chapter (Chapter 2) summarizes theoretical and empirical studies of
currency crises. It will also discuss the link and implications of currency crises
theories with empirical studies that deal with the application of EWS models. The
chapter will also highlight the advantages and disadvantages of these standard
EWS models. In addition, an alternative EWS model, namely the ANN model, is
included so as to address the limitations of the EWS models. Chapter 3 provides an
overview of the Indonesian economy, particularly in the period before, during and
after the 1997/98 Asian Financial Crisis. It will define currency crises in Indonesia
using the definition adopted by Kaminsky et al. (1998).
The first part of Chapter 4 will summarize the empirical studies that apply the
signal approach to predict crises. The signal EWS model using 55 monthly leading
indicators from 1970 to 1995 will then be adopted to test its predictive power using
the out-of-sample currency crises from 1996 to 2008. In order to define the cause of
crises, this chapter will also develop sector specific signal models.
The fifth chapter develops the probit/logit model for predicting currency crises in
Indonesia. This involves a summary of previous empirical studies on currency
crises in which the probit/logit model has been applied. The noise-to-signal ratio
as used by the signal approach that selects the set of explanatory variables will
then be used. The study will then develop and compare two probit models, namely
the general and specific models as Kamin and Babson (1999) and Kamin et al.
(2007) employed in their studies. These models will be estimated and tested using
data from 1971 to 1995 so as to predict the out-of-sample currency crises from 1996
to 2008.
The sixth chapter employs the artificial neural network model as an alternative
EWS model. This chapter also summarizes previous empirical studies that have
applied this method to predict crises. This chapter will also develop and compare
two models, namely the general and specific models. The general model uses the
6
set of input neurons from the top ten ranked leading indicators that are based on
their noise-to-signal ratio, while the specific model uses five highly significant
explanatory variables based on the probit model. Both models are then trained
using the in-sample period from 1971 to 1995 and tested to predict the out-of-
sample crises from 1996 to 2008.
Chapter 7 evaluates and compares the performance of these three EWS models
using a 24-month pre-currency crisis window for both in-sample and out-of-
sample periods. The second part of this chapter will then test the sensitivity and
consistency of these models when the assumption of a crisis window is shortened
to 6, 12, and 18 months. The latter part of Chapter 7 will also evaluate and compare
the performance of these models in predicting these shortened crisis windows.
Finally, Chapter 8 will summarize the main findings related to the application of
these EWS models for predicting Indonesian currency crises. It will highlight
possible future directions for research that will enable improvement and extension
of the use and application of these EWS models.
7
CHAPTER 2
LITERATURE ON THE EWS MODELS
2.1. Introduction
Throughout modern history, currency crises have impacted upon the fortunes of
many countries. According to Bordo et al. (2001), while currency crises occurred
even in the nineteenth century, their frequency and the number of countries hit by
crises has increased appreciably following the collapse of Bretton Woods in the
1970s. Furthermore, in the last three or four decades, the frequency and the
severity of currency crises have increased due to globalization and the rapid
development of information technology (Saxena, 2004). Furthermore, according to
Bordo et al. (2001), the impact of crisis became a dominant feature in the 1990s,
striking many countries in Europe (ERM crises in 1992/93, Russia in 1998/99),
Asia (Asian Financial Crisis in 1997/98) and Latin America (Mexico in 1994, Brazil
in 1998/99).
The 2007 Subprime Crisis in the United States was followed by the 2008 Global
Financial Crisis. Crisis has become a major issue, attracting a great deal of attention
from multilateral organizations, governments, press and the public, due to the
magnitude of the impact and huge recovery costs of these crises. Today, crises still
pose a serious threat to many countries around the world in general and European
countries in particular. Previously countries that were affected by crisis were
dominated by developing countries, but the recent crises shows that developed
countries are also vulnerable to attacks. In other words, there are no countries,
either developed or developing, that can avert the ramifications of crises.
The increasing frequency of crises and number of countries hit by crises as well as
the magnitude of the impact and the huge recovery costs have encouraged many
8
scholars to explain these phenomena and to determine the common underlying
factors. As mentioned in Chapter 1, since the Asian Financial Crisis, various
models have been developed in order to predict the occurrence of crises. Success in
this area should allow policy makers to take counter-cyclical action in order to
avoid the crises or to minimize the impact. Therefore, this chapter will summarize
previous studies, their approaches of explaining these crises, and will ask if there is
a common determinant. Highlighted will be the contribution of these various
studies to the development of empirical models in predicting crises. Finally, in the
last part of this chapter, various crisis forecasting models used in previous studies
will be discussed and lessons will be drawn in an effort to develop useful models
for predicting currency crises, with particular emphasis being placed on their
ability to cater for Indonesia.
The organization of this chapter will proceed as follows. Section 2.2 discusses the
theoretical literature on currency crises. Section 2.3 analyzes their implication for
the constructing of early warning system (EWS) models. Section 2.4 discusses the
forecasting methods in predicting currency crises. Section 2.5 will conclude this
chapter.
2.2. Theories and Models of Currency Crisis
This section presents a brief review of the theoretical literature on the causes of
currency crises. In general, the series of crises that hit many countries around the
world cannot be said to have a common foundation (Saxena, 2004, Kaminsky, 2006,
Esquivel and Felipe, 1998). According to Kamisky (2006), they can be grouped into
six types of crises of which four are related domestic vulnerabilities, such as
deterioration of the current account, fiscal imbalance, financial crisis, and external
debt crisis; while the other two types are categorized as sudden-stop crisis, which
is caused by massive capital flight, and the self-fulfilling crisis, caused by panic
9
among investors and herd behavior. It is also noted that these various types of
crisis also exhibit differences in the common underlying factors, as well as the
severity of impact and the cost and time of recovery. Furthermore, Kamisky (2006)
points out that the crisis with the worst impact is the financial crisis.
It is noted that crises experienced by developing countries and more advanced
economies tend to differ. In emerging economies a crisis is most commonly
associated with multiple domestic vulnerabilities, while in the more mature
economies external vulnerabilities are the most common cause. Thus, shocks
emanating from international capital markets can cause sudden-stop and self-
fulfilling crises (Kaminsky, 2006).
Also noted is that the main features and nature of the various crises that have
occurred in many countries around the world, including emerging markets and
mature economies, are not the same as they change over time (Saxena, 2004,
Kaminsky, 2006, Esquivel and Felipe, 1998). For examples, Saxena (2004) pointed
out that the crisis, which occurred in Latin America during 1970s and 1980s was
different from the European crisis in 1990s, this being the ERM crisis in 1992/93. It
is also shown that there are common patterns across these crisis episodes (Esquivel
and Felipe, 1998).
In this regards, many papers have been written and various theories have been
developed to explain the causes of speculative attacks on domestic currencies, and
to define the common underlying factors associated with these crises. These papers
and theories can be broken up into three groups or generations, namely the first
generation crisis, the second generation crisis and the third generation crisis
models. The next sub-sections will briefly describe the specific characteristics or
features of these three generations of crisis models.
10
2.2.1. The First Generation Model
The first generation crisis model was inspired by the collapse of the exchange rates
in Latin American countries during the 1970s and 1980s, for example the Bolivian
crisis (1982-85), the Brazilian crises (1983, 1986 & 1989–90), the Chilean crisis (1971-
74), Peruvian crises (1976 & 1987) and the Uruguayan crisis (1982) (Saxena, 2004).
The first generation of speculative attack model is based on the paper of Krugman
(1979) which typically describes the balance of payment crisis in Latin America
during this period. It focuses on the role of inconstant policies (Chinn, 2006),
macroeconomic fundamentals and speculation (Breuer, 2004). Within this
framework, a currency crisis is seen as a sudden fall in the level of foreign reserves
caused by an attack on the domestic currency and the inevitable change in the
exchange rate regime. Thus the currency crisis here is actually the outcome of poor
macroeconomic policy and of rational arbitrage by speculators.
The crises occurred when the Latin American countries financed their budget
deficits, mostly exceeding 8–10% of GDP (Saxena, 2004), by increasing central bank
credit to the government (Krugman, 1979, Rangvid, 2001), or by monetizing their
budget deficits (Esquivel and Felipe, 1998). Krugman (1979) argued that this policy
lifted the amount of money supply to exceed the amount of money demand. As a
result, according to Rangvid (2001) the foreign exchange reserves depleted at the
same rate as the increase of domestic credit. As persistent loss in foreign reserves
continued, governments abandoned the fixed exchange rate policy due to a
speculative attack on domestic currency, which finally led to a currency crisis
(Rangvid, 2001, Kaminsky, 2006, Esquivel and Felipe, 1998), or as interpreted by
Krugman (1979), as a balance of payment crisis.
2.2.2. The Second Generation Model
The first generation crisis model fails to explain the European Monetary System
(EMS) collapsis in 1992/93, because the roots of this crisis did not come from the
11
depletion of foreign reserves (Kaminsky, 2006). Moreover Jeanne (2000) mentioned
that the EMS crisis was caused by declining credibility of governments in the EMS
countries when dealing with problems of high unemployment and high interest
rates that resulted from the unification of Germany. Similarly, Kaminsky (2006)
argued that the EMS crisis was caused by the conflict between maintaining the
fixed exchange rate and other objectives of government policies, such as reduction
of inflation and achieving economic growth. While maintaining a fixed exchange
rate regime helped achieve the first goal, it reduced competitiveness, and this led
to recession. In addition, abandoning the peg by devaluing a currency increased
competitiveness and ultimately boosted economic activity and reduced
unemployment. As a result, Jeanne (2000) argued that the EMS crisis encouraged
the emergence of the second generation crisis model but that this was not due to
the problems of economic fundamental alone but rather to the nature of the
relationship between the fundamental and speculative attacks against domestic
currencies.
While the crisis that hit EMS countries was initially triggered by the reunification
between West Germany and East Germany in 1990, in order to improve the living
standard of people of the former East Germany, the government adopted an
expansive fiscal policy. This saw a substantial increase in public spending spurred
by the need for investment in infrastructure and by the rise in unemployment
compensation. This pushed inflation up and placed an upward pressure on real
interest rates in Germany to ease the inflationary pressure. At the same time, under
the European Regional Monetary regime, capital became perfectly mobile across
the European borders, resulting in the high interest rates in Germany attracting
capital inflows from other EMS countries that led to the appreciation of the
Deutsche mark. As a response, the other EMS countries raised their interest rates to
maintain the balance of payments equilibrium. Because the countries were in
recession and suffered high unemployment, the policy of raising interest rates
12
encouraged speculators to attack domestic currencies, such as the British pound,
Italian lira and French franc. This later spread to other currencies as they believed
that it was too costly for the respective governments to maintain the fixed
exchange regime. This crisis is illustrated clearly by Eichengreen et al. (1993) using
a simple two-country model in the tradition of Mundell-Fleming.
Using this crisis model, Obstfeld (1986, 1996) found that the expectation of people
was an important trigger for crises. Similarly Flood and Marion (1999) argued that
the shift in market expectation also caused government trade-offs that created self-
fulfilling crises. For example although countries had sound macroeconomic
fundamentals, as people expected currency devaluation in the near future, they
placed enormous pressure on local currency by converting their currency to a
foreign currency before the central bank devalued its currency. This eventually
depleted the foreign reserve used by central banks to defend their currency – thus
a self-fulfilling crisis. Furthermore, the 1992/93 EMS crisis with the feature of ‘self-
fulfilling’ also indicates that a speculative attack can happen even when countries
have sound macroeconomic fundamentals, as mentioned by Flood and Marion
(1999).
2.2.3. The Third Generation Model
Of the three generations mentioned earlier, the first-generation model was inspired
by Latin American crises in the 1970s and 1980s that focused on monetary and
fiscal crises, while the second-generation crisis model, inspired by the EMS crisis in
earlier 1990s, focused on trade-off of government policies and the features of self-
fulfilling crises. On the other hand, the Mexican crisis in 1994 and the Asian
Financial Crisis in 1997/98 encouraged the emergence of a new crisis model
(Kaminsky, 2006). The third-generation of currency crisis models focused on the
issue of contagion and self-fulfillment (Berg and Pattillo, 2000), these being
triggered by the moral-hazard and imperfect information relating to the economic
boom, international lending and asset price bubble (Saxena, 2004, Kaminsky, 2006).
13
Furthermore, Berg and Pattillo (2000) argued that the herding behavior of investors
could have caused the contagion effect that spread crises in Asian countries in
1997/98 through the financial market. This crisis model also highlights the closed
relationship between currency crises and banking crises (Saxena, 2004, Kaminsky
and Reinhart, 1999).
However, according to Berg and Pattillo (2000) the causes and the underlying
factors that led to these crises both in the Mexico crisis in 1994 and the Asian
Financial Crisis in 1997/98 are different. The Mexican crisis originated from the
self-fulfilling crisis that was driven by deteriorating domestic fundamentals such
as the appreciation of the Peso. This led to a current account deficit coupled with
high external debt. This was dominated by short-term external debt for both
government and private sectors, leaving them vulnerable to speculative attacks on
the Mexican peso (Berg and Pattillo, 2000). On the other hand, the occurrence of
the Asian Financial Crisis and the severity of its impact surprised many parties as
for decades these countries had experienced high and stable economic growth and
sound fiscal conditions.
It is believed that the Asian Financial Crisis was caused by weaknesses in the
corporate and financial sectors (Sharma, 1999, Berg and Pattillo, 2000, Radelet and
Sachs, 1998a) that suffered because of high exposure of their debt to the currency
imbalances. According to Radelet and Sachs (1998a), the problem in these countries
initially started when the Asian countries applied financial reform that liberalized
their financial sectors, particularly banking sectors. Unlike the developed
countries, in emerging economies, including the East Asian countries, the banking
sector played a pivotal role in providing the funds for the private sectors compared
to the capital markets (Berg and Pattillo, 2000). Compounding the problem was
that the number of banks increased significantly, a feature that boosted credit
expansion and capital inflows. The fixed exchange rate regime (Basri and Rahardja,
2010, Radelet and Sachs, 1998a) and low international interest rate also encouraged
14
the banking and corporate sectors to borrow abroad. The high capital inflows into
these countries was also triggered by the sound performance of Asian economies
for a few decades previous to the crisis periods, while at the same time Europe and
Japan experienced relatively weak economic performance (Webber, 2001).
According to Radelet and Sach (1998a) and Webber (2001) financial liberalization
that is followed by excessive credits and huge capital inflows can increase the
vulnerability of a financial sector. As the huge capital inflows to the East Asian
countries was dominated by private and commercial bank lending and equity
investment, largely short-term and mostly un-hedged, to finance unsound projects
or non-productive sectors and long-term projects, such as real estate, this created
price bubbles and increased debt exposure to the currency imbalance (Miller and
Luangaram, 1998, Webber, 2001). This situation increased the vulnerability of the
East Asian economies through their financial sector as it increased the exposure of
these economies to capital outflows whenever the perception of investors and
confidence shifted (Miller and Luangaram, 1998, Webber, 2001).
As mentioned by Miller and Luangaram (1998), overinvestment and overvaluation
led to weaknesses in corporate sectors and combined by inadequate policy
responses taken by governments in addressing problems in the banking sector led
to sudden loss of confidence which triggered systemic panic among investors and
created self-fulfilling crisis. Furthermore, according to Webber (2001) because of
the flight to safety, investors started to withdraw their short-term funds. This led to
sharp depreciation in the domestic currency, followed by the fall value of property
and equity values.
In addition to the domestic factors, some scholars have indicated that external
factors also contributed to the occurrence of this crisis. For example, the rise of
world interest rates in 1994 made the difference between interest rates smaller, a
situation that shifted the perspective of investors inducing them to relocate their
15
funds from developing countries to developed countries – a case of ‘flight to
quality’. In addition, Esquivel and Felipe (1998) argued that before the occurrence
of the Asian Financial Crisis, Asian countries experienced a combination of
currency appreciation and deterioration in their current accounts. According to
Miller and Luangaram (1998), Caramazza et al. (2004) and Webber (2001), as the
East Asian currencies were pegged to the US dollar, when US dollar appreciated
against the Yen by 50% from 1995 to 1997, this caused the East Asian currencies to
appreciate. This, in turn, reduced their competitiveness against China and Mexico
through NAFTA, and combined by a slowdown in world trade and weak growth
in Japan this caused a decline in their exports, which later led to deterioration in
the current account balance. This was particularly the situation that occurred in
Thailand and Korea. As mentioned by Edwards (1989) the combination of the
currency overvaluation and the current account deterioration led to currency
devaluation. As their currency was pegged to the US dollar, investors might have
assumed that maintaining the peg would be costly for the government, thus
driving down the foreign reserve and triggering a speculative attack. The result
would be a self-fulfilling crisis, placing additional pressure on the domestic
currency, and leading to a financial crisis.
2.3. The Currency Crisis Models and Predicting Crisis Models
Following the Asian Financial Crisis, studies not only focused on explaining the
occurrence of crises and defining the underlying causal factors but tended to
develop models to predict the occurrence of crises. But, these efforts cannot be
separated from the previous studies on explaining crises because according to
Sharma (1999) the provision and selection of data sets is crucial and a challenging
task when developing EWS models, and the EWS model performance is also
largely determined by the availability of such data. The previous section has
16
explained that not only do crises differ and change over time but that the factors
that cause crises, can be divided into three generations of crisis models. This
section focuses on the implication of these three crisis theories on development of
EWS empirical models by suggesting the variables that might be leading indicators
in predicting crises.
As previously mentioned, according to this first-generation crisis model, a crisis
occurs when foreign reserves persistently decline because of a monetary budget
deficit that leads to a speculative attack on the domestic currency. Based on the
crisis model, the relevant leading indicator is fiscal imbalance and credit to the
public sector (Kaminsky, et al. 1998). In addition, Esquivel and Felipe (1998) found
that seignorage, RER misalignment, the current account balance, and the log of
M2/reserves, are significant variables for the first-generation crisis model.
In addition, related to the empirical work of predicting financial crises, the second
generation crisis model suggests some leading indicators that may be useful for
preventing and predicting financial crises. As already noted, the second-generation
crisis model was inspired by the ERM crisis in 1992. This crisis was caused by the
conflict of the objectives of the authorities and the actual policies adopted.
Therefore, the variables that potentially lead to this crisis can be used as the
leading indicators in predicting the currency crises, especially the crisis based on
this second-generation model, such as output, domestic and foreign interest rate,
unemployment rate, inflation rate, the amount and composition of external debt,
financial fragility, etc. In addition, as mentioned by Abiad (2002) and Kaminsky et
al. (1998) the real exchange rate, the trade or current account balance, and real
wages have the potential to increase the likelihood of a crisis. Similarly, Esquivel
and Felipe (1998) found that negative terms of trade shocks, negative per capita
income growth, and a contagion effect coupled with the leading indicators of the
first-generation crisis model can explain the occurrence of this kind of crises. This
17
crisis model also emphasizes the feature of self-fulfilling, which means that crisis
can occur without the change in fundamentals.
The third generation crisis model also attempts to explain the occurrence of the
Asian Financial Crisis in 1997/98. Prior to the crisis, the Asian countries
experienced high economic growth and robust fiscal conditions, therefore many
scholars pointed out that in this crisis the fundamentals played an insignificant
role. As mentioned by Kaminsky et al. (1998), prior to this crisis, these countries
only experienced a few of this set of eleven leading indicators, which commonly
present themselves prior to a crisis, such as a low level of foreign reserves, severe
currency crises appreciation, high domestic credit growth, high proportion of
credit to the public sector, high domestic inflation, deterioration of the trade
balance, declining exports, excessive money growth, a low ratio of foreign reserves
to narrow money, declining real GDP growth, and increasing public deficits. As a
result, many scholars point out that this crisis was generally caused by the
contagion effect and a self-fulfilling crisis through financial markets. Berg and
Pattillo (2000) found that the only drawback was in their corporate and financial
sectors. Therefore, the leading indicators that can be used to explain and predict
the occurrence of such crisis would certainly be related to these two sectors.
Chinn (2006) argued that the source of this crisis came from contingent liabilities.
Similarly, Caramazza et al. (2004) argued the large short-term obligations, maturity
mismatch of liabilities, a low ratio of foreign reserves to short-term external debt
can increase the risk of having crises due to investor shifts that lead to self-
fulfilling. Kaminsky (2006) pointed out that high foreign debt levels, or indicators
related to fiscal crises, such as government deficits, or even indicators related to
stock and real estate market booms and bursts can be used as good leading
indicators for the third-generation crisis model.
18
Kaminsky (1998) and Caramazza et al. (2004) also argued that the risk of crises
increased in the countries with weak domestic banking systems. Kaminsky (1998)
lists the leading indicators related to the banking problems, including the relative
price of bank stocks, the proportion of nonperforming loans, central bank credit to
banks, and a large decline in deposits. Moreover, the political variables can also be
used as a leading indicator that may have increased the uncertainty that led to the
investor shift (Kaminsky, et. al, 1998).
Caramazza et al. (2004) argued that the financial linkages and weaknesses, such as
reserve adequacy and maturity of bank liabilities, can be used to explain the
contagion effect that helped spread crises from one country to others. In addition,
Kaminsky (1998) and Caramazza et al. (2004) also argued that the crisis in a
neighboring country can increase the risk exposure in the other neighboring
countries, thus encouraging investor panic, leading to a self-fulfilling crisis related
to a flight to safety that leads these countries into crisis.
2.4. Predicting Currency Crises and the Role of EWS Models
The severe impact of the Asian Financial Crisis and the Mexico crisis in the mid
1990s put a big question mark on the effort to define the crises and to determine
the underlying factors that caused them, and possibility to predict them (Esquivel
and Felipe, 1998). Since these crises, the study about a currency crisis has not only
focused on explaining the occurrence of crises and defining the underlying factors
that caused the crisis itself, but has extended to developing EWS models to predict
a crisis. This study follows that path.
Related to the empirical work for predicting currency crises, the three generations
of crisis models highlighted in the previous section, may suggest a set of
explanatory variables that are important in developing a EWS model to predict a
19
currency crisis. These previous studies indicated that crises are different and by
nature the underlying factors of currency crises change overtime. Until today there
are three generations of crisis models. The latest generation crisis model comes
with the features of the contagion effect and is self-fulfilling. According to this
model, crises can occur without any change in fundamentals, which makes the
effort to predict the crises much harder and even multilateral institutions like the
World Bank and the Asian Development Bank and rating agencies were not able to
predict the Asian Financial Crisis. In fact, a few months prior to the crisis, they still
estimated the East Asian countries would experience stable economic growth for
the rest of the 1990s.
In addition, the rapid development in information technology coupled with rapid
integrated global financial markets implies that a crisis in one country can increase
the likelihood of a crisis hitting other countries faster, consequently, the least time
available for the authority to prevent the occurrence of a crisis. Moreover, as
indicated by the sub-prime mortgage crisis in USA in 2007, the rapid innovation
and development of financial products, such as derivative products, also increases
the vulnerability of one country to fall into crises. The above challenges make the
effort to develop a EWS model to predict crises even more difficult.
According to Berg and Pattillo (2000) the EWS model can combine all information
from various sets of explanatory variables or leading indicators into a single
vulnerability index. Basically the EWS model can provide information about the
probability of a crisis or early information on the vulnerability index of one
country before a crisis occurs. Based on these information, the authorities or policy
makers can have time to take counter-cyclical actions in order to eliminate the
crisis, or at least to reduce its impact. As mentioned by Esquivel and Felipe (1998),
good policies can prevent the occurrence of a crisis, in this sense, the EWS model
can help to prevent the recurrence of currency crises. As previously mentioned,
after the Asian Financial Crisis, a number of models have been created that attempt
20
to help decision makers in predicting future crises, but, broadly speaking, two of
the most popular EWS models are the signal approach, which is a non-parametric
approach, and the probit/logit model, which is the parametric approach.
The signal approach was pioneered by Kaminsky and Reinhart (1996) and
Kaminsky et al. (1998). According to Kaminsky (2006) the basic idea of this model
is based on the fact that the set of leading indicators behave differently during the
crisis and tranquil periods, and the anomalous behavior of the set of leading
indicators prior to the crisis period can be used for predicting a currency crisis.
Using this method, the anomalous behavior of these indicators can be transformed
into warning signals if they pass their specific thresholds, allowing these weighted
signals of selected leading indicators to be transformed into a single composite
index. Furthermore, for ease of interpretation, the composite index is converted
into the probability of a crisis.
The application of this model is simple and straightforward as it does not need to
put restrictions in its data sets (Sharma, 1999). As this model first analyzed each
leading indicator individually before combining them into one composite index,
this model can inform the list of deviant behavior of its leading indicators, as well
as the overall probability of a crisis. Thus policymakers can pay more attention to
this set of leading indicators as they contribute more to the vulnerability. In
predicting a crisis, the signal model is preferred when using a long and high
frequency data set (Zhuang and Dowling, 2002), and also can use a large number
of leading indicators, as mentioned by Kaminsky et al. (1998) and Eliasson and
Kreuter (2001) because it ignores the correlation among leading indicators.
However, according to Abiad (2002) this model also has some drawbacks, such as
it cannot provide the marginal effects of its leading indicators, and cannot
distinguish two or more leading indicators that move together. Neither can it be
tested using a statistic test, which makes it more difficult to compare its
performance with others. In addition, there is no special software dedicated to this
21
model. A more detailed explanation of this signal model and the application of this
model for predicting the Indonesian currency crises will be presented in Chapter 4.
On the other hand, the application of the discrete choice model, that is the
probit/logit model in estimating models of currency crises, was popularized by
Eichengreen et al. (1996) and Frankel and Rose (1996), and was then adopted by
other scholars. This model not only estimates the probability of a crisis, but it can
also inform the statistically significant explanatory variables that determine the
probability of the crisis itself (Sharma, 1999). Another advantage of this model is
that it can evaluate all explanatory variables simultaneously (Sharma, 1999, Abiad,
2002) and can inform the marginal effects of these variables relative to the
probability of a crisis (Komulainen and Lukkarila, 2003). In addition, this model
can be estimated using standard econometric or statistical softwares (Abiad, 2002).
However, unlike the signal approach, this model also has some disadvantages, for
using too many explanatory variables may lead to multicollinearity problems
(Jacobs et al., 2005, Zhuang and Dowling, 2002), and a potential increase of noise in
the estimation results and the number of statistically insignificant explanatory
variables (Kamin et al., 2007). Furthermore, using a high frequency data set, such
as monthly data, also has the potential to make noise in estimation results due to
imbalances in the sample. This is because of too few months being included in
crisis periods compared to the number of months in tranquil periods (Esquivel and
Felipe, 1998). In addition, according to Kaminsky et al. (1998) this model cannot
measure and rank their explanatory variables based on their ability to predict
crises. A more detailed explanation of this model, the survey of previous studies
using this model in estimating currency crises, and the current application of this
model in predicting Indonesian currency crises can be seen in Chapter 5.
Even though the previous studies applied these standard EWS models, it is argued
that these models have succeeded in identifying vulnerabilities. Esquivel and
22
Felipe (1998) indicated that currency crises can be predicted, for their model is able
to predict currency crises in most of their samples. However, Sharma (1999) argued
that predicting the timing of a crisis is very difficult, while Chinn (2006) argued
that crises are not entirely predictable. Similarly, Berg and Pattillo (1999a) pointed
out that their prediction models only just outperformed random guessing.
However, when using the long horizons, Berg and Pattillo (2000) found that while
the performance of these EWS models in predicting currency crises has so far has
been mixed, as on one side these models are able to capture the potential risk of
crises but on the other the models still produce lots of false alarms.
Due to the mixed prediction capacity of the timing of crises, these models cannot
substitute the instinctive judgment that has been widely practiced by policy-
makers (Bussiere and Fratzscher, 2002, Zhuang and Dowling, 2002). For this
reason, as mentioned by Edison (2000) and Kaminsky et al. (1998), many
economists and scholars have attempted to find alternative models. Accordingly,
this study has also developed an alternative EWS model in predicting currency
crises, especially for Indonesia, by applying an artificial neural network (ANN)
model.
The selection of this model is more due to the limited application of this model in
predicting currency crises than its success stories when used for predicting
purposes in other fields. This is because it is a non-linear model that has superior
features, not found in the standard models, such as fault tolerance, generalization,
and adaptability. In establishing a EWS model, the availability and reliability of
data sometimes become a major constraint, particularly when the analysis needs
long term historical data. However, this problem has limited impact on the
application of the ANN model because of its ability to deal with erroneous,
incomplete or missing, fuzzy or noisy input data (Kamruzzaman et al., 2006).
Moreover, unlike other models, the ANN model can be trained to obtain any
desired accuracy results. But this model has some drawbacks too, especially as it
23
requires large computational analysis and has a tendency for over-fitting. In
addition, as a black-box model, it cannot explain the causal relationship between
input and output. More detailed explanation of this ANN model, the previous
application of this model, as well as this current study which applies this
alternative model to predict the Indonesian currency crises are presented in
Chapter 6.
2.5. Conclusion
The series of currency crises that hit many countries around the world over many
years are not the same; they can be classified into six types of crises. Similarly the
underlying factors associated with these crises also changed over time. They can be
classified into three generations of crisis models. These crisis models might suggest
a list of explanatory variables or leading indicators that will probably be useful for
enhancing the performance of EWS models. However, according to the latest
generation of crisis models, features of contagion effect and self-fulfilling will
make the effort of predicting a crisis more difficult, as it can occur without a
change in the fundamentals. In addition, the projection becomes more difficult as
the trend of globalization and rapid integration of capital markets makes the time
for policy makers to react become less. Finding the increase in probability of a
crisis becomes more difficult because of the likelihood of crisis in other countries
being transmitted quickly due to the integration of capital markets. The subprime
mortgage crisis in USA in 2007 illustrates that the development and innovation of
financial products or derivative products can also make the effort to predict crisis
much more difficult.
There are various models used by some scholars to predict currency crises, but two
are very popular models, namely the signal and the discrete choice probit/logit
models. However, crises are becoming more difficult to be predicted, although
24
previous application of EWS models indicates that crises can be predicted even if
their results still vary and some prove unreliable. To overcome this problem,
developing new techniques or methods can accelerate the finding of more reliable
methods, and towards that goal, this study employs an alternative EWS model,
namely the artificial neural network. Compared to EWS models, these models
show positive performance but they also suffer from several weaknesses.
25
CHAPTER 3
THE INDONESIAN CURRENCY CRISIS EPISODES
3.1. Introduction
Indonesia, like other countries in the world, is susceptible to speculative attacks
on its currency. Since 1970, the country has experienced a number of crises. Two
have attracted particular attention from the public, namely the 1997/98 Asian
Financial Crisis and the Global Financial Crisis of 2008. The interest associated
with the 1997/98 crisis has been based on the magnitude of the impact, while
the Global Financial Crisis, although not so significant in terms of its impact on
Indonesia, has attracted more public attention. This is because of the process of
government intervention in addressing the crisis, particularly its bank bailout
policy at the end of 2008.
For three decades before the 1997/98 Asian Financial Crisis Indonesia
experienced high economic growth, perhaps explaining why no agencies or
institutions appear to have been interested in estimating the likelihood of this
crisis. However, thereafter, in an effort to prevent a repeat of such incidents, the
attention of academia and international agencies was directed at finding ways
to predict crises. As mentioned in the previous chapter, this study sets out to
build a model to predict currency crises in Indonesia by using an early warning
system (EWS). An initial step in the process of building the EWS model is to
determine the periods of currency crises. Therefore the objective of this chapter
is to provide a brief overview of the currency crises that have occurred in
Indonesia, with special attention being paid to the Asian Financial Crisis of
1997/98, including its impact and the recovery process. The study also attempts
to determine the period of currency crises that will be used in the empirical
models.
The chapter is organized as follows. Section 3.2 describes the reversal of
fortunes - ‘from miracles to crises’. Section 3.3 focuses on identifying the causes
26
of crises. Section 3.4 highlights the long road to economic recovery. Section 3.5
defines the currency crisis dependent variable. Finally, Section 3.6 presents a
summary with concluding remarks.
3.2. Reversal of Fortunes: From Miracles to Crises
3.2.1. Prior to the Crisis
Following 1966 when the New Order regime came to power, Indonesia
experienced a high growth averaging 7.4% per year for the whole period, and
7.6% per year from 1990 to 1997 (see Table 3.1). The figures reflect the positive
impact on the prosperity and improved welfare in the country, which was
characterized by a decreasing level of poverty, rising income per capita,
increased life expectancy and reduced infant mortality. For example, comparing
1966/67 and 1996/97, per-capita income rose from $75 to $1200, the poverty
level decreased from 60% to 11% (or 22 million people), infant mortality
decreased from 118 to 52 per 1,000 births, and the average life expectancy
increased from 48 to 64 years (Baker, 1998).
The World Bank (1993) acknowledged this achievement when including
Indonesia as a newly industrialized economies (NIEs) along with two other
ASEAN countries, Thailand and Malaysia1. The World Bank also recognized the
three countries together with Japan and the “four tigers” of Asia, namely the
Republic of Korea, Singapore, Hong Kong and Taiwan, as being members of the
East Asia miracle, and of the eight high-performing East Asian economies
(HPAEs) (The World Bank, 1993). On the basis of this achievement, the export-
oriented development strategy of all these countries became the role model for
other countries in developing their economies (Wie, 2003).
1 ASEAN stands for the Association of Southeast Asian Nation, which established on August 8, 1967 in Bangkok, Thailand.
27
TABLE 3.1 GDP Growth of Asia-5 (% per annum)
Country 1990 1991 1992 1993 1994 1995 1996 1997 Average (90-97)
Indonesia 9.0 8.9 7.2 7.3 7.5 8.2 8.0 4.6 7.6
Malaysia 9.6 8.6 7.8 8.3 9.2 9.5 8.6 7.8 8.7
Philippines 3.0 -0.6 0.3 2.1 4.4 4.8 5.7 5.1 3.1
South Korea 9.5 9.1 5.1 5.8 8.6 8.9 7.1 5.5 7.5
Thailand 11.6 8.1 8.2 8.5 8.6 8.8 5.5 -0.4 7.4
Source: International Monetary Fund (1998)
By looking at the impressive performance of Indonesia's economy over the
three decades in Table 3.1, it is not surprising that the existence and depth of the
crisis of 1997/98 was not anticipated (Grenville, 2004). Prior to the crisis, there
was no clear signal of the possibility that an economic downturn would occur
because of Indonesia’s supposedly strong macroeconomic fundamentals.
Furthermore, a few months before the crisis, the World Bank (1997) released a
report pointing out that Indonesian economic growth was expected to remain at
7.8% for the remainder of the 1990s. Similarly, the long-term debt ratings
predicted that stable economic performance would occur over the 18 months
run-up to the crisis.
TABLE 3.2 Moody’s and Standard and Poor’s Long Term Debt Ratings for Indonesia Prior to Asian Financial Crisis, 1996-1997
15/1/96 2/12/96 24/6/97 12/12/97
Rating Outlook Rating Outlook Rating Outlook Rating Outlook
Moody’s Foreign Currency Debt
Baa3 Baa3 Baa3 Baa3
S&P’s
October 1997
Foreign Currency Debt
BBB Stable BBB Stable BBB Stable BBB Negative
Domestic Currency Debt
A+ A+ A- Negative
Note: Rating Systems (from highest to lowest), Moody’s: Aaa, Aa1, Aa2, Aa3, A1, A2, A3, Baa1, Baa2, Baa3, Ba1, Ba2, Ba3; Standard and Poor’s ( S&P’s): AAA, AA+, AA, AA-, A+, A, A-, BBB+, BBB, BBB-, BB+, BB, BB Source: Radelet and Sachs (1998b).
3.2.2. Indonesia in Crisis
The Asian Financial Crisis that occurred in 1997/98 stemmed from pressure on
the Thai baht, which forced the Thailand government to float its currency on 2
July 1997. It resulted in regional negative sentiments from neighboring
28
countries including Indonesia. To respond to pressure on the Indonesian
rupiah, on 11 July 1997, Bank Indonesia first widened the intervention band
from 8 to 12%, followed on 14 August 1997 by a change in the exchange rate
system - a shift from a controlled and managed float to a free-floating exchange
rate regime.
In addition, Bank Indonesia implemented a tight monetary policy and
sterilization by raising interest rates from 10.5 to 20%, so as to prevent capital
outflow. But the combination of a large depreciation of the Rupiah, plus high
interest rates, made the crisis deeper (Nasution, 2000), for the decline of the
Rupiah caused an increase in the debt of the private sector which was
dominated by foreign currencies, particularly the US dollar, which was mostly
unhedged (Green and Campos, 2001). Furthermore the implementation of a
tight monetary policy caused many banks to experience liquidity difficulties,
resulting in August 1997 with more than 50 banks failing to comply with the
minimum reserve requirement of 5%. (Djiwandono, 2000). Meanwhile, the
government implemented strict fiscal policy by cutting unnecessary routine
spending, as advised by the IMF, which according to Nasution (2000)
exacerbated the depth of the crisis.
As the crisis deepened, to enhance public confidence and to overcome the crisis,
the government on 8 October 1997, requested help from the IMF through a
fully-fledged standby arrangement. Although initially the public response was
quite positive, things changed after the first instalment of the IMF economic
stabilisation program on 1 November 1997 which set out to liquidate 16 of 26
insolvent banks, constituting 3% of the total assets of the banking sector. As a
result, the banks closure policy, in the absence of a deposit insurance scheme
led to public panic and loss of confidence in the banking industry. This was
followed by a massive bank rush, particularly on private domestic banks, due
to “flight to quality”, which later on boosted liquidity of the state and foreign
private banks. In other words, Indonesia was hit by a banking crisis following
the liquidation of the 16 banks. Furthermore, the capital flight continued to
29
reach $600-700 million per day, causing the Rupiah to depreciate further to a
new low of Rp17,000 per US$1 on 22 January 1998.
In an effort to restore public confidence and halt depreciation of the Rupiah and
the capital flight, the second IMF letter of intent was signed on 15 January 1998.
Then, three months after the closure of the 16 banks in November 1997, the
government on 27 January 2008, finally implemented a blanket guarantee
scheme for commercial banks, domestic and foreign (the obligation included
depositors and creditors). In addition, the Indonesian government established
the Indonesian Bank Restructuring Agency (IBRA) as an independent body
under the Ministry of Finance to deal with bank restructuring. This was in turn
followed by establishment of the Indonesian Debt Restructuring Agency
(INDRA) in September 1998, and the Jakarta Initiative Task Force (JITF) in
November 1998, to deal with the corporate debt restructuring outside the court
system (Manring, 1999).
However, the condition was still inconclusive and became worse due to
widespread student protests and riots in several big cities, especially in Jakarta,
that became known as the tragedy of May 1998 (Tragedi Mei 1998). The events
that occurred from 12 to 14 May 1998, were followed on 21 May 1998 by the
resignation of President Soeharto, after 32 years as President of the country.
3.2.3. The Impacts of the 1997/98 Asian Financial Crisis
The Asian Financial Crisis in 1997/98 that hit Indonesia was the worst crisis to
hit the country since 1970 (Asia Times, 1999). It affected not only on the
economic sector but also other sectors, and had social and political implications.
It has been viewed as a multi-dimensional crisis (Adiningsih et al., 2008).
The economic costs caused by this crisis were devastating. On the fiscal side,
the government budget was under tremendous strain, reeling under the impact
of weaker economic activity; lower export performance was affected by lower
global oil prices; and subsidies increased as a result of the depreciation of the
Rupiah which reached Rp17,000 per US$1 on 22 January 1998. Inflation also
30
climbed to 82.4% in September 1998. In the banking sector, an increasing
number of banks experienced a negative balance during this period, further
affecting their capital base. In October 1998, the equity of the private national
and the seven state banks dropped to the negative zone, and thus became
insolvent. Finally the combination of these factors resulted in a slowing of
Indonesian economic growth in 1997, although it remained positive at 4.6%,
before contracting by 13.1% in 1998.
The social impact of the crisis was also highly detrimental, as it eliminated the
government’s success in overcoming poverty over the previous three decades.
As a result, the proportion in poverty rose from 11.28% or 17.72% (depending
on which measure is taken)2 in 1996 and to 24.2% in 1998 (Balisacan et al., 2002).
The unemployment rate also increased significantly. Furthermore, the impacts
of the economic crisis spilled over, to pose a threat to national security and
integrity. This was mainly due to the rising racial and religious tensions that
accompanied the acceleration of economic difficulties faced by some elements
of Indonesian society. The impact engendered both social and political disorder,
culminating in the Jakarta riots that resulted in the resignation of Soeharto as
President of Indonesia on 21 May 1998 (Martinez-Diaz, 2006, Smith, 2003, Hill,
2007, Soesastro, 2000).
On the other hand, the 1997/98 crisis also contained positive elements, as it
created a more transparent and accountable system of government and politics,
enhanced the decision-making process and also eliminated barriers that led the
way to corporate and financial system reform, as well as reform in the spheres
of law and justice (Manring, 1999). According to Feridhanusetyawan and
Pangestu (2004), this crisis caused a significant change in terms of economic,
politic and social spheres within Indonesia, especially notable was the shift
from an authoritarian regime to a democratic nation. It also saw a shift from a
centralized to decentralized government, and from state led development,
2 This number is based on the new method of poverty calculation held by Statistics Indonesia.
31
oligopoly and monopolistic market structure, to become more open and
competitive.
3.3. Identifying the Causes of the 1997/98 Asian Financial Crisis
The severity of the impact on the society encouraged experts and academics to
examine the underlying reasons for the crisis in an effort to prevent such an
event occurring again. The opinions of experts and academics when
determining the cause of the 1997/98 Asian Financial Crisis can be split into
two, namely the emphasis on external shocks or the contagion effect, and
domestic fundamental weaknesses.
3.3.1. External Factors: Financial Contagion
The 1997 financial crisis in Asia was, in some circumstances, different from the
crises that hit the Latin America countries in the 1980s, or the ERM crisis in
1992. In that sense it calls for a new theoretical framework to be developed to
explain the Asian Financial Crisis of 1997/98 (Kaminsky, 2006). Previously,
crises were deemed to be the outcome of poor macroeconomic policy and of
rational arbitrage by speculators, as explained by the first generation of crisis
models (Krugman, 1979, Flood and Garber, 1984), or as being caused by the
trade-off between the fixed exchange rate policy and other government
objectives, known as the second generation of crisis model (Kaminsky, 2006).
Prior to the 1997 economic crisis, Indonesia experienced high economic growth,
had a robust fiscal position, and had displayed a moderate inflation rate over
many years. So, in the case of the 1997 Asian crisis that hit Indonesia and other
Asian countries, many scholars and economists believed it was triggered by the
contagion effect of the Thai baht crisis that led to regional negative sentiment,
which in turn encouraged speculative attacks on the rupiah and other regional
currencies. That is to say, economic fundamentals played only a small role in
generating the financial crisis.
32
3.3.2. Internal Factors: the Weakening of Domestic Economic Fundamentals
According to this view, prior to 1997, domestic economic fundamentals in
Indonesia were strong enough to allow high economic growth for decades that
was sufficient to allow it to become one of the newly industrialized countries.
The only weakness was found in the financial and corporate sectors that came
to be the channel primarily responsible for the collapse of the Asian ‘miracle’
economies, including Indonesia. Many have blamed the premature opening up
of the Indonesian economy, particularly its financial sector in the 1980s, as the
primary cause of the crisis (Pincus and Ramli, 1998).
At the beginning of the “new order” regime led by President Soeharto,
particularly during the period 1966 - 1980, the government relied heavily on oil
revenues, but when world oil prices fell in the early 1980s, recognised by Hill
(2000) as the end of the oil-financed growth decade, the government began
promoting non-oil exports. To increase competitiveness of the non-oil products,
the government devalued the Rupiah and liberalized the financial system by
issuing several policy packages, namely the package in June 1983 known as
“Pakjun”, and followed up in October 1988 with “Pakto”. Under the former
1983 regulation, the government negated its control over interest rates, removed
subsidies on deposit rates in state banks and credit limits for all banks, reduced
subsidized liquidity credits, replaced the entire ineffective credit ceiling with
monetary instruments and Bank Indonesia certificates known as “SBI”
(Feridhanusetyawan et al., 2000). Furthermore, the October 1988-policy package
gave a greater role to both the local and foreign private sectors to facilitate the
establishment of banks and branch offices.
These policy packages were followed by an increase in the number of banks,
particularly private banks, changes in market structure and competition
between banks, as well as a boost in credit availability for sustaining high
economic growth (Feridhanusetyawan et al., 2000, Pangestu, 2003). For
example, the number of banks more than doubled from 111 in 1988 to 239 in
1996 (see Figure 3.1). Furthermore, the amount of outstanding loans also rose
sharply from Rp12.83 trillion in 1983 to Rp340 trillion in July 1997
al., 2007). The rapid growth of credit
sector, may have been responsible for overheating the economy
economic bubble prior to the crisis
FIGURE 3
Source: Bank Indonesia (various years)
In response to the excessive credit
another policy package in February 1991 known as “Pakfeb”
at tightening liquidity
restrictions such as
before issuing credits
was facilitated by the op
external borrowing climbed significantly
rising to US$55 billion in mid
less than one year (Pincus and Ramli, 1998
On the other hand,
accompanied by the availability of prudent banking regulation and
supervision from Bank Indonesia
7 7
66
14411
30
27
27
0
50
100
150
200
250
1988 1992
State Bank National Private Bank
33
sharply from Rp12.83 trillion in 1983 to Rp340 trillion in July 1997
The rapid growth of credit, which dominantly went
may have been responsible for overheating the economy
prior to the crisis.
3.1 The Development of Banks in Indonesia, 1988
Bank Indonesia (various years)
esponse to the excessive credit allocation, the government
policy package in February 1991 known as “Pakfeb”
tightening liquidity through the banks that were required to introduce
such as minimum capital, and loan to deposit ratio requirements
before issuing credits. This policy led to an increase in foreign borrowing that
was facilitated by the open capital account. As it turns out,
borrowing climbed significantly, resulting in the accumulated debt
billion in mid-1997, with US$34.2 billion payment
Pincus and Ramli, 1998).
hand, the rapid growth in the banking sector
by the availability of prudent banking regulation and
Bank Indonesia (Pangestu, 2003). Problems that emerged were
7 7 7 7 7
161 166 165 164144
39 40 41 41
44
2727 27 27
27
1992 1993 1994 1995 1996 1997
National Private Bank Foreign/joint venture bank
sharply from Rp12.83 trillion in 1983 to Rp340 trillion in July 1997 (Zulverdi et
went to the real estate
may have been responsible for overheating the economy and creating an
Development of Banks in Indonesia, 1988-1999
allocation, the government introduced
policy package in February 1991 known as “Pakfeb”. This was directed
that were required to introduce
loan to deposit ratio requirements,
. This policy led to an increase in foreign borrowing that
en capital account. As it turns out, the private sector’s
the accumulated debt
payment being due in
banking sector was not
by the availability of prudent banking regulation and strong
Problems that emerged were
7 7
130
92
44
40
27
25
1998 1999
Foreign/joint venture bank Regional bank
34
also due to rampant corruption, collusion and nepotism between rulers and
businessmen. According to Schwarz (1994), such conduct was a disease that
struck the Indonesian nation at that time. Many saw such unhealthy practices as
the main cause of the economic crisis in 1997 (Pincus and Ramli, 1998). As
presented by Stiglitz and Greenwald (2003) weak institutional infrastructures,
such as the law, law enforcement and proper and prudent controls, encouraged
banks to take high risks in their operation, leading to moral hazards.
Furthermore, lack of control and weak supervision resulted in a variety of
offenses in the Indonesian banking sector, such as over-issuing credit limits and
bank guarantees, especially to companies that were engaged in funding the
expansion of their subsidiaries (Zulverdi et al., 2007).
Excessive credits thus eventually rendered the economy vulnerable as the
economy overheated. This viewpoint is supported by the study of Demirgue-
Kunt and Detragiache (1998), who studied 53 countries including developed
and emerging markets from 1980 to 1995. They concluded that there was a
strong connection between financial liberalization and financial fragility,
whereby economic fragility occurs a few years after liberalization. Similarly,
Bordo et al. (2001) also mentioned that the financial liberalization and
inefficiency of financial markets in the distribution of resources increase the
frequency of crises and the magnitude of the impact of crises in the 1990s. In
line with this argument, Kaminsky and Reinhart (1999) identified the close
relation between banking and currency crises following financial liberalization.
According to Caprio and Summers (1993), the lack of effective institutions to
oversee prudential regulation and supervision, and the absence of well-
functioning capital markets and stable legal systems, offset the benefits of
financial liberalization by increasing a country’s vulnerability to banking crises.
35
3.4. After the 1997/98 Asian Financial Crisis
3.4.1. The Long Road to Economic Recovery
As mentioned in the previous section, in responding the 1997/98 Asian
Financial Crisis, the Indonesia authorities applied various policies, starting with
widening the intervention band on the Rupiah, adopting a free-floating
exchange rate regime, tightening monetary policy, adopting strict fiscal policy,
and finally, asking for help from the IMF. Furthermore, when restructuring the
severely damaged banking system, the authorities also applied policies such as
bank closures, implemented blanket guarantees, and established new
institutions, such as IBRA (Indonesian Banking Restructuring Agency), INDRA
(Indonesian Debt Restructuring Agency) and JITF (Jakarta Initiative Task
Force). However, compared to other Asian countries affected by the crisis, the
recovery process in Indonesian was much slower. The country’s relatively slow
recovery reflected the scale and complexity of the Indonesian crisis, which
brings into focus the transitional conditions in redefining the Indonesian state
in terms of its economic, political and social life (Feridhanusetyawan and
Pangestu, 2004).
Nasution (2002) argues that the slow process of recovery could have been due
to internal and external factors that followed the financial crisis of 1997/98. For
internal factors, the “new order” regime, characterized by authoritarian and
centralized government, was followed by a transitional democratic and
decentralized era. This was associated with weak central government coupled
with weak fiscal capacity to stimulate the economy because of declining tax
revenues and falling export earnings caused by low world oil prices. In addition
there was an increase in expenditure for external and domestic debt financing
and for a high level of subsidies. The unrecovered banking sector limited its
intermediary function and, coupled with political and social instability, this led
to declining investor interest in Indonesia. Externally, factors such as the
international recession after the terrorist attacks on 11 September 2001 also
contributed to the slow economic recovery.
36
Martinez-Diaz (2006), while stressing that the financial crisis of 1997/98 was the
worst crisis to hit Indonesia, also highlighted the severe political crisis as
contributing to a slow process of economic recovery. In addition, Wie (2003)
mentions the natural disaster that accompanied the “el nino” and which
resulted in a long dry season for many parts of Indonesia, as being another
reason for the slow recovery process. This problem was coupled with the health
problems of President Suharto that reduced public confidence in the ability of
government to overcome the crisis (Manring, 1999).
Moreover, Cole and Slade (1999) argued that the initial policy of the
government in dealing with troubled banks and the closure of 16 banks in
November 1997, exacerbated the crisis and adversely affected recovery. As the
impact of this policy was usually permanent and required immediate
settlement to creditors and debtors, it also had a psychological impact on public
confidence. On the other hand, neighbouring countries had the same banking
problems, but the policies taken to address these issues were different, as they
only froze the troubled banks, resulting in a temporary impact.
The Indonesian economy reached the bottom of the crisis in 1999, after which
macroeconomic stability was gradually restored, although full economic
recovery even today has a long way to go. Compared to other countries in the
region, such as Thailand, Korea, and Malaysia, Indonesia’s economic recovery
was slower. While its economy grew less than 1%, Korea, Thailand, and
Malaysia grew by 9.5%, 4.4% and 6.6% respectively.
After implementing various economic reforms, the Indonesian economy finally
escaped from the crisis and as a result, it came out of the IMF program at the
end of 2003. Since 2004, as shown in Figure 3.2, GDP grew around 5.5% per
year, even though during this period the recovery process was challenged by
various threats from both domestic and external factors. For example, the
political turmoil that followed the dismissal of the fourth President
Abdurrahman Wahid in July 2001; the devastating natural disaster associated
with the earthquakes and tsunami in Aceh and North Sumatra in December
37
2004; the rise of world commodity prices, particularly oil, that pushed the
government to remove the subsidy on the domestic fuel price and to increase
the price twice, in May and October 2005. This became recognized as a mini
crisis (Titiheruw et al., 2009, Imansyah and Abimanyu, 2008).
FIGURE 3.2 Indonesian GDP Growth, 1991-2007
Sources: The World Bank (2011), Statistics Indonesia (2011), Adiningsih et al. (2008)
Despite escalating world oil prices and the continuing fallout from the
‘subprime mortgage’ crisis in the United States, the year 2007 was a special time
for the Indonesian economy. This was because the year not only marked the
tenth anniversary of the Asian economic crisis, but it was the first time the
Indonesian economy grew at pre-crisis rates, when reaching 6.3%; see Figure
3.2. The improved level of purchasing power and investment primarily drove
this achievement.
In terms of supply, the main contributors to economic growth were the
manufacturing, trade and agricultural sectors. While the growth of
manufacturing during 2007 reached a respectable rate of 4.7%, trade, and
restaurant and hotel businesses, increased by over 8.5%. Similarly, the
8,90
7,20 7,30 7,508,20 7,80
4,60
-13,10
0,60
4,90 4,903,80
4,404,90
5,60 5,606,30 6,00
-15
-10
-5
0
5
10
1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
38
agricultural sector enjoyed a positive trend growth of 3.5% as a result of robust
export demand and high prices for international commodities.
TABLE 3.3 GDP Growth by Sectors, 1996-2007
Source: Statistic Indonesia (2011), Bank Indonesia (various years)
Sustainable but modest economic growth rates over the last decade, which hit a
peak in 2007, contributed to reinvigorate the standard of living, as indicated by
per capita income that reached US$1,946. Unemployment also fell from a high
of just over 11% in 2005 to 8.4% in 2008, while 4.5 million new jobs were
provided; see Figure 3.3. In addition, the poverty rate declined continuously to
reach 15.4% in 2008, equal to a reduction of about 1.9 million people living in
poverty. Furthermore according to the Ministry of National Development
Planning/National Development Planning Agency, known as BAPPENAS
(2010), the implementation of poverty alleviation programs in Indonesia proved
successful, as the percentage of people having per capita income less than US$1
per day reached 8.5% in 2007 to then decline further to 5.9% in 2008. This was
remarkably lower than the actual target of 10.3% set by the Millennium
Development Goals (MDG) for 2015.
1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007
Agriculture 3.14 1.00 -1.33 2.16 1.88 4.08 3.23 4.34 2.12 2.49 2.72 3.50
Mining & Quarrying 6.30 2.12 -2.76 -1.62 5.51 0.33 1.00 -0.89 -4.94 1.59 3.72 2.00
Manufacturing 11.59 5.25 -11.44 3.92 5.98 3.30 5.29 5.33 6.38 4.63 4.58 4.70
Electricity, Gas & Water Supply 13.63 12.37 3.03 8.27 7.56 7.92 8.94 5.88 4.23 6.49 6.07 10.40
Construction 12.76 7.36 -36.44 -1.91 5.64 4.58 5.48 6.67 6.91 7.34 9.09 8.60
Trade, Hotels, & Restaurants 8.16 5.83 -18.22 -0.06 5.67 4.38 3.90 5.30 5.78 8.59 8.95 8.50
Transportation & Communication
8.68 7.01 -15.13 -0.75 8.59 8.10 8.39 11.56 14.02 12.97 13.64 14.40
Finance, Rental & Business Services
6.04 5.93 -26.63 -7.19 4.59 6.60 6.37 7.02 7.90 7.12 5.27 8.00
Services 7.77 4.59 -13.24 0.61 4.86 3.83 4.38 4.88 4.89 5.60 5.55 6.60
GDP 7.77 4.59 -13.13 0.61 4.86 3.83 4.38 4.88 4.89 5.60 5.55 6.30
39
FIGURE 3.3 Indonesian Social Indicators, 1999-2008
Sources: The World Bank (2011), Statistics Indonesia (2011), Adiningsih et al. (2008)
3.4.2. The 2008/09 Global Financial Crisis
Following the high economic growth over the previous few years, early in 2008,
Bank Indonesia (2008) predicted continuing growth of between 6.2 to 7.4% over
the next few years. However, the resilience of the Indonesian economy has since
been challenged by the Global Financial Crisis, originally triggered by the 2007
sub-prime mortgage crisis in the United State of America. According to
Murniningtyas (2009), this crisis may affect the Indonesia economy through the
stock market, and through shortages in the capital market and production. Basri
and Rahardja (2010) point out two channels by which this crisis may affect
developing countries, including Indonesia, namely through financial and trade
channels.
Regarding financial contagion, Titiheruw et al. (2009) state that the Indonesian
financial sector had no direct exposure to the sub-prime mortgage securities, as
no Indonesian banks operated abroad, and furthermore, Indonesian banks are
not allowed to conduct securities transactions in capital markets, and no
47,97
38,7 37,9 38,4 37,336,1 35,1
39,337,17
34,96
23,43
19,14 18,41 18,217,42
16,6615,97
17,7516,58
15,42
6,36 6,07
8,19,06 9,5 9,84
11,2210,28
9,18,4
0
10
20
30
40
50
60
0
5
10
15
20
25
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
# of poverty(in million, RHS) Poverty rate (LHS) Unemployment rate (LHS)
40
investment banks are permitted to operate in the Indonesian banking system.
Even so, this crisis has led to an increased investment risk in emerging markets
and the global liquidity shortage has spread negative sentiments in financial
markets (Bank Indonesia, 2009). Likewise, Basri and Rahardja (2010) have
indicated the impact of this crisis on the Indonesian financial sector is highly
significant. For example, the composite stock price index dropped dramatically
from 2830 on January 2008 to 1155 on 20 November 2008 because of the
withdrawal of a foreign investor who was the dominant player in the
Indonesian stock market when holding 67% of total equity market
capitalisation. As a result, the Rupiah depreciated significantly against the US
dollar, dropping in value by 30% between October and November 2008 (Basri
and Rahardja, 2010). This pressure continued and led to the massive sell-off of
Bank Indonesia certificates (SBI) and government securities This pushed the
increase of the yield of government bonds from 10 to 20% (Ministry of Finance
of Republic of Indonesia, 2010). As a result, and according to Titiheruw et al.
(2009), in the last quarter of 2008, the foreign ownership of government
securities fell sharply from US$11.1 to $8 billion; similarly, the foreign
ownership of Bank Indonesia certificates dropped from US$2.2 billion to $772
million.
In the other channel of the crisis, trade, the pressure on the economy of
Indonesia hit the external sector in which export growth fell to 1.82%, or the
lowest since 1986 (Titiheruw et al., 2009). This was in line with the slowdown in
economic growth of Indonesia’s major trading partners, as well as with world
commodity prices (Titiheruw et al., 2009, McCulloch and Grover, 2010, Basri
and Rahardja, 2010). However, due to the structure of Indonesia’s economy
being dominated by the domestic economy, a decline in exports has not
seriously affected it. The reason for this is that domestic consumption exceeds
external consumption, for as was mentioned by Statistic Indonesia (2011), the
contribution of domestic consumption in the economy reached 69% in 2008 but
only slightly dropped to 68% in 2009. In the same period, exports only
contributed around 30% in 2008 before dropping to 24% in 2009, therefore the
41
decline in exports did not seriously affect the Indonesian economy (McCulloch
and Grover, 2010, Basri and Rahardja, 2010). Moreover, when compared with
neighbouring countries, the impact of this crisis on the Indonesian economy has
been much lower, as its contribution of exports in the economy has been much
lower than in those other countries (McCulloch and Grover, 2010).
3.4.3. The Comparison between the 1997/98 Asian Financial Crisis and the
2008/09 Global Financial Crisis
Although in terms of scale the 2008/09 Global Financial Crisis has been much
bigger than the 1997/98 crisis, especially as it has affected more countries
around the world, its overall impact on the Indonesian economy has been more
limited (Basri and Rahardja, 2010). This is emphasised by McCulloch and
Grover (2010) who claim that the impact of the 2008 crisis on the Indonesian
economy is not large, its impact only causing a slowdown in economic growth
due to a decline in exports. Unlike the 1997/98 crisis which resulted in an
economic slowdown to 4.6% in 1997 followed by a 13.1% economic contraction
in 1998, the recent crisis saw growth at the relatively high rate of 6% until the
third quarter of 2008 after which it dipped in the fourth quarter to 5.2%, a figure
less than the previous estimation of 5.7% (Titiheruw et al., 2009). For the whole
year of 2008 the average rate of economic growth was 6%, after which it
dropped to 4.7% in 2009, thus slightly higher than the low in the previous crisis.
Likewise, the social impact of this crisis was much lower than during the 1997
crisis, for the number of poor fell from 15.42% in 2008 to 14.42% in 2009, or to
2.43 million people. The unemployment level also declined from 8.4% to 8.1% in
2009, although as seen in table 3.4, this was one feature that was higher than the
level reached in 1997/98.
Moreover, Basri and Rahardja (2010) have argued that the limited impact of the
crisis of 2008/09 compared to the 1997/98 crisis has been associated with the
difference in the sources of the crises, the pre–conditions in terms of the
financial sector, the political situation, and policy measures taken by the
government. The lesser impact can also be attributed to management plans
42
implemented by the government that helped to sustain public trust, along with
its efforts to limit depreciation of the Rupiah, and accelerate the recovery
process (McCulloch and Grover, 2010). More detail on the differences between
these two crises can be seen in Table 3.4.
In terms of policy responses taken by government, unlike the 1997/98 crisis, to
overcome the 2008/09 crisis, the government tended to pursue policies that
differed from those adopted in the first crisis. For example, its monetary policy,
whereby Bank Indonesia cut interest rates gradually from 9.5 to 6.5% between
October 2008 and August 2009. Also, in order to boost the economy it
maintained domestic purchasing power and reduced the operating costs of the
business sector. The government also enlarged the budget deficit by 2.6% of
GDP to provide a package of fiscal stimulus valued at Rp73.3 trillion, or US$6.4
billion, through tax reductions, by providing diesel and electricity subsidies,
and by developing infrastructure and rural sector projects (Basri and Rahardja,
2010). In addition, the government also continued the provision of direct cash
transfer programmes for the poor amounting to Rp100000 per month, or US$8,
for 18.2 million of targeted poor households for two months, while at the same
time it increased the salaries of government and military officials and
pensioners (Titiheruw et al., 2009). Those policy decisions are reflected in the
high level of government consumption that recorded growth of 10.4% in 2008
and a further increase to 15.7% in 2009. This led to an increase in the
contribution of government expenditure in GDP formation to nearly 10% in
2009.
When dealing with the banking distress, unlike the previous crisis when the
government closed the troubled banks, the government adopted bailout
strategies to combat the situation. Thus its reaction to the Century Bank where
it intervened in order to avoid a banking crisis, while at the same time avoiding
the psychological impact of bank closures in order to maintain public
confidence in the domestic banking sector. However, a year later, this policy
became a hot political issue between legislators and government, who argued
43
regarding the objectives and the underlying reasons behind that policy
decision. The issue became a trendy topic for the press and public during 2010,
and continues to be discussed today.
TABLE 3.4 The Difference between the 1997 Asian Financial Crisis and the 2008/09 Global Financial Crisis on Indonesian Economy
The 1997 Asian Financial Crisis The 2008/09 Global Financial Crisis
Macroeconomic and Financial Indicators, origin of crisis and political condition
GDP 4.7% 6.1%
Inflation 11.05% 11.06%
External Sector
-Current Account* -2.3% 0.1%
-International reserve 21.4** (5.5***) 51.6** (4.0***)
-Foreign debt* 62.2% 29.0%
Fiscal Account
-Fiscal balance* 2.2% 0.1%
-Public debt* 62.2% 32%
Banking Sector
-LDR (%) 111.1% 77.2%
-CAR (%) 9.19% 16.2%
-NPL (%) 8.15% 3.8%
Origin of crisis Unclear and debatable between external factor (contagion effect from Thai-Bath crisis) and poor economic and financial fundamentals
External factor which is trigger by the sub-prime mortgage crisis from United States of America
Politic condition Unstable due to political crisis Stable
Policy Responses
Monetary policy Strict monetary policy and sterilization by raising interest rate
Reduction interest rate by 300 basis points from 9.5 to 5%
Fiscal policy Tight fiscal policy by cutting unnecessary routine spending to keep budget surplus and later on change to allow budget deficit
Allow budget deficit for providing fiscal stimulus packages by reduction of taxes, and provide subsidies for electricity and fuel, direct cash transfer for the poor and increase the salaries of government and military officials and pensioners
Banking policy Liquidation or closure 16 Banks Then after three months followed by blanket guarantee
Bailout bank i.e. Bank of Century Increase in the coverage of deposit guarantee by Indonesian Deposit Insurance Corporation from Rp100 million to Rp2 billion
Impact of crisis
-Unemployment rate (%)
4.7% (5.5% in 1998) 8.46% (7.9% in 2009)
-Poverty rate (%) 24.2% (in 1998) 15.42 (14.2% in 2009)
-# of poor 49.50 million (in 1998) 34.96 million
-Income per capita (US$)
1,299 2,000
* (%of GDP); ** International reserve (billions of USD); *** (month of imports and official foreign debt repayment; Source: Bank Indonesia (2009), Statistic Indonesia (2011), Basri and Rahadja (2010)
44
3.5. Defining the Currency Crisis
Although the previous sections have discussed the currency crises that occurred
in Indonesia, the exact duration of these crises have not been clearly disclosed.
Therefore, in this section, an attempt will be made to determine the actual
period of financial crisis, as this will be useful as a first and crucial step for
developing the empirical models for predicting currency crises in the following
chapters.
What constitutes a crisis is a crucial factor but there is no agreed definition of a
currency crisis among academics. Some scholars have described a crisis as a
“speculative attack” or “extreme pressure” on the exchange rate that leads to a
substantial sharp change in the exchange rate (Kamin et al., 2007, Frankel and
Rose, 1996). Nevertheless, such definitions only take into account a successful
speculative attack on the exchange rate which shows up in the form of
depreciation of foreign exchange in a free floating exchange rate regime, or that
of official devaluation of the exchange rate in a fixed exchange rate regime.
In the event of a speculative attack on a domestic currency, the central bank can
retain the exchange rate through monetary policies, either increasing official
interest rates or selling off foreign reserves. As a result, unsuccessful
speculative attacks can be seen either in the fall of foreign reserves or in the rise
of interest rates (Krznar, 2004). Following these arguments, Eichengreen et al.
(1995) define a crisis as being large movements in exchange rates, foreign
reserves, and interest rates. However, as many emerging markets had interest
rate controls, Kaminsky et al. (1998) defined a crisis as a situation whereby an
attack on the currency leads to a sharp depreciation of the currency, or to a
large decline in international reserves, or a combination of these two
circumstances. These currency crisis definitions encompass both successful and
unsuccessful attacks on the domestic currency, whether they are under a fixed
exchange rate regime or under other sorts of exchange rate regimes.
45
In light of this, crises are identified by the behaviour of an “exchange market
pressure index”, or, in short, EMPI. Based on Goldstein et al. (2000), the EMPI is
calculated as the weighted average of the change in the direct quoted nominal
exchange rates (ER), and the change in foreign exchange reserves (FR), of which
the weight (ω) is the ratio of the standard deviation of the rate of change of the
exchange rate to the standard deviation of the rate of change of reserves, or as
follows:
����� = ���� − �������� � − ���� − �������� ����������������������(3.1)�
Based on this equation, an increase in the EMPI index indicates more pressure
on a domestic currency, which is shown by a depreciated domestic currency or
decline in the foreign reserve or the combination of these conditions.
The next step is to transform EMPI into binary crisis variables in which the “1”
represent a crisis when EMPI is equal or bigger than a given threshold,
otherwise “0” for no crisis. While, the threshold (L) in question is chosen as a
given number of standard deviations from the EMPI’s mean. According to
previous empirical work, the value of the threshold for EMPI varies from 1.5
(Eichengreen et al., 1996) to 3.0 (Goldstein et al., 2000) standard deviations
above the mean of EMPI. Symbolically,
��� = �10� ��������� ≥ ��������� < ��� �������������������������������������������(3.2)�
In determining a currency crisis, following Kaminsky et al. (1998) and Goldstein
et al. (2000), the EMPI is calculated using Equation 3.1. Its thresholds are also
calculated using EMPI’s mean plus three EMPI’s standard deviations, as
presented in Figure 3.4. In this figure, the EMPI is presented as a red line and its
threshold is in black. Furthermore, following the work of Kaminsky et al.
(1998), a currency crisis is defined whenever the EMPI passes its thresholds, so
that the yellow shaded area is the 24 months prior to the currency crisis date.
46
FIGURE 3.4 EMPI, Thresholds, and Currency Crises Episodes
Using Figure 3.4, Indonesia experienced four episodes of currency crisis from
January 1970 to September 2008. However, unlike the other currency crises,
during the 1997/98 Asian Financial Crisis, the EMPI crossed its threshold five
times, in August and December 1997, plus January, May and June 1998. The
period of the in-sample currency crises, which was determined by this method
also coincides with the policy of the government of the Republic of Indonesia
which devalued the Rupiah in 1978, 1983 and 1986 in order to support the
export-oriented growth (Goeltom, 2008) and to encourage the performance of
non-oil exports as a response to declining world oil prices in the 1980s (Hill,
2000). As Mishkin (1999) mentioned that in developing countries, currency
devaluation and currency crisis could generate the financial crises through the
corporate debt, which was denominated in foreign currency and at short
maturity, which lead to a deterioration in the balance sheet of the corporate and
banking sectors as well as an increase the inflation rate. In addition, the period
of currency crises determined by using this approach is quite similar to the
period of currency crises in the previous studies in this field, in particular for
the in-sample currency crises, as displayed in Table 3.5.
-0,4
-0,2
0
0,2
0,4
0,6
0,8
1
1,21
/1/1
97
01
/1/1
97
11
/1/1
97
21
/1/1
97
31
/1/1
97
41
/1/1
97
51
/1/1
97
61
/1/1
97
71
/1/1
97
81
/1/1
97
91
/1/1
98
01
/1/1
98
11
/1/1
98
21
/1/1
98
31
/1/1
98
41
/1/1
98
51
/1/1
98
61
/1/1
98
71
/1/1
98
81
/1/1
98
91
/1/1
99
01
/1/1
99
11
/1/1
99
21
/1/1
99
31
/1/1
99
41
/1/1
99
51
/1/1
99
61
/1/1
99
71
/1/1
99
81
/1/1
99
91
/1/2
00
01
/1/2
00
11
/1/2
00
21
/1/2
00
31
/1/2
00
41
/1/2
00
51
/1/2
00
61
/1/2
00
71
/1/2
00
8
a 24-months prior crisis date EMPI Treshold
47
TABLE 3.5 The Indonesian Crises Episodes Based on Previous Studies
No EWS Study Crisis Periods
1. Goldstein et. al. (2000) Nov 1978, Apr 1983, Sep 1986, Aug 1997
2. Zhuang & Dowling (2002) Nov 1978, Apr 1983, Sep 1986, Dec 1997
3. Edison (2000) Nov 1978, Apr 1983, Sep 1986
4. Current Study Nov 1978, Apr 1983, Sep 1986, Aug 1997, Dec 1997, Jan 1998, May 1998, Jun 1998
3.6. Conclusion
Prior to 1997, Indonesia was a country that experienced high economic growth
to become one of the Asian economic miracle countries. But the Asian Financial
Crisis that hit Indonesia became a major catastrophe not only in economic but
also social and political terms. The causes of this crisis have since been debated
in academic circles. Some argue that the crisis was associated with the
contagion effect from Thailand’s crisis while others believe the crisis came from
deterioration of domestic fundamentals, especially in the financial sector
because of financial liberalization in 1980s.
Although the recovery process in Indonesia was initially slow when compared
to other Asian countries affected by crisis, after completing the IMF program in
2003, the Indonesian economy grew around 5.5%, even though the recovery
process was challenged by many obstacles during this period, including the
political crisis of 2001, and an earthquake and tsunami in 2004, plus a mini-
crisis in 2005. After a decade passed, Indonesia was finally able to grow at a rate
similar to that experienced in the period before the crisis, reaching 6.3% in 2007.
But unfortunately the resilience of the Indonesian economy was challenged by
the Global Financial Crisis in 2008, which originally came from the sub-prime
mortgage crisis in United States of America. The crisis affected the Indonesian
economy in the last quarter of 2008 through the financial contagion effect that
downgraded the perception of foreign investors on the investment risk in the
Indonesian financial sector. This was reflected by a massive selling-off of stocks,
48
Bank Indonesia certificates and government securities. This crisis also led to a
decline in exports caused by slowing economic growth in major trading
partners and falling world commodity prices. However, unlike the 1997/98
Asian Financial Crisis, the effect of the recent crisis was not too significant as it
only caused a slowdown in economic growth to 6% in 2008, with a further drop
to 4.7% in 2009.
In determining the period of currency crisis that will be used as the dependent
variable in developing models to predict currency crises in the following
chapters, and based on the method of Kaminsky et al. (1998), this study has
found that Indonesia experienced four currency crises during 1970 to 2008.
49
CHAPTER 4
PREDICTING INDONESIA CURRENCY CRISES
USING THE SIGNAL MODEL
4.1. Introduction
As already explained in previous chapters, the purpose of this study is to
develop models that can predict currency crises in Indonesia. Currently, many
forecasting techniques and methods are available to predict Indonesian
currency crisis episodes using early warning system (EWS) models. However,
this study only considers three EWS models and this chapter is the first chapter
of three that are dedicated to developing these EWS models. For this purpose,
this chapter employs and extends the signal model pioneered by Kaminsky et
al. (1998).
The discussion in this chapter is organized as follows. Section 4.2 describes the
previous literature of the application of the signal EWS model. Section 4.3
explains the signal EWS model including the design of the system, depicts the
scope of the model, and explains the performance evaluation methods to be
used. Section 4.4 focuses on the application of the signal model as a EWS model
for predicting Indonesian currency crises both for the in-sample and out-of-
sample periods. Section 4.5 develops the sectoral specific signal model followed
by concluding remarks in Section 4.6.
4.2. A Survey of Empirical Signal EWS Models
In relation to the empirical work of predicting financial crises using the signal
EWS model, the literature of currency crisis models and theories provides some
leading indicators that may be useful for improving the performance of EWS
models. Kaminsky et al., (1998) conducted an extensive survey of empirical
50
work on currency and financial crises that was aimed at identifying the
potential leading indicators for their work on predicting a currency crisis. The
authors also provided a ranking of leading indicators based on their forecast
capability as measured by their noise-to-signal ratio, plus the time and
persistence of their signals. In other works, Kaminsky and Reinhart (1998) and
Kaminsky (1999) constructed a signal EWS model to predict currency and
banking crises in 20 countries, including 5 developed countries and 15
emerging economies. Their model was able to accurately forecast the 1997/98
Asian financial crisis with the exception of Indonesia.
In related work, Goldstein et al. (2000) extended the research by replacing the
developed countries with other emerging economies and added more leading
indicators. They found that in the case of emerging economies, it was more
difficult to forecast banking crises than currency crises due to the difficulties in
identifying an accurate and recurrent banking crisis. They also showed that
there is a wide discrepancy of performance across leading indicators. To
overcome these problems, they developed a method of aggregating the best
indicators into a composite indicator of a currency crisis. Like the previous
work, they were able to predict the Asian financial crisis for all Southeast Asian
countries with the exception of Indonesia.
In the Indonesian crisis, despite the unstable political condition at that time,
they pointed out that the unavailability of indicators to capture the contagion
effect was the main reason why their model did not send any alarm prior to the
1997 crisis. This argument is also supported by Goldstein et al. (2000) who
indicated that Indonesia had a high contagion vulnerability index related to the
1997 Thai crisis that occurred during the Asian financial crisis.
Edison (2000) extended the signal model developed by Kaminsky et al. (1998)
and Kaminsky and Reinhart (1999) to detect financial crises. She found that the
prediction results were mixed. As Kaminsky (1999) and Goldstein et al. (2000),
she also failed to identify the 1997 crises in Indonesia. Furthermore, Furman
51
and Stiglitz (1998) also employed the signal method to predict the Asian
financial crisis and concluded that it did not work well.
Berg and Pattillo (1999a) evaluated three existing models by comparing their
predictive power to other models including the probit model of Frankel and
Rose (1995), the standard regression model of Sachs et al. (1996), and the signal
model of Kaminsky et al. (1998). Their results found that only one model, i.e.,
the signal model of Kaminsky et. al (1998), was able to predict a crisis; however,
their results are mixed and unreliable. Moreover, their models also fail to
predict the 1997 Indonesian crisis. Nevertheless, they still believe that even
though the predictive powers of these EWS models are limited, they may help
to indicate future vulnerability. Similarly, Edison (2000) highlighted that the
signal EWS model was a useful tool for identifying vulnerabilities.
The above-mentioned studies applied the signal model to predict the crisis
using multi-country data. However, using cross-country analysis can be
advantageous, as researchers can compare the result across countries. Yet, the
selection of indicators can sometimes be limited when trying to capture the
specific characteristic for the whole sample, as compared to a regional or a
single country analysis. In relation to the previous empirical work, this
limitation means that prediction results will be mixed. For Indonesia, most of
their selected leading indicators failed to send any warning signals (Goldstein
et al., 2000).
On the other hand, using regional studies, Zhuang and Dowling (2002)
employed a signal EWS model to identify the source of the Asian financial crisis
during 1997 in six Asia countries: Indonesia, Thailand, Malaysia, Philippines,
Singapore and South Korea. For this purpose, they used a set of 38 leading
indicators and managed to predict the Asian financial crisis in five out of six
countries: Indonesia, Thailand, Malaysia, Philippines, and South Korea. It can
be envisaged that the main source of the crisis in these countries was the
deterioration of domestic fundamentals. However, in the case of Singapore,
their model could not encounter any signal of financial crisis, since the main
52
factor attributed to each crisis suggests financial contagion and investor panic
rather than domestic fundamentals. For Indonesia, they found six crisis
episodes from 1970 to 1997, namely 1970, 1971, 1978, 1983, 1986, and 1997. For
the 1997 financial crisis, its probability remained low from 1987 to 1996 but
started to climb to around 70% for the seven consecutive months before the
Indonesian rupiah depreciated by 21.5% in December 1997. Similar results were
found by Imansyah and Abimanyu (2008) when applying a signal model for
predicting currency crises in Indonesia. Using 22 leading indicators, their model
was able to capture the 1997 currency crisis. These two studies identified that
flexibility in selection of leading indicators can improve the performance of the
model in predicting a crisis. Following this argument and to improve the
predictive power of the signal EWS model, this study will expand the number
of leading indicators and the sample time period to see whether this model can
capture recent crises. Following this, a detailed explanation of the signal EWS
model will be presented in the following section.
4.3. Methodology
There are some important steps in developing the signal EWS model. It starts
with defining the dependent variable of currency crisis, and is then followed by
selecting a set of leading indicators that can determine the currency crisis. The
next step will be to transform the movement of indicators into the warning
signal for each indicator. They will then be combined into one index that will
represent the whole selected leading indicators based on their contributions to
predicting a crisis. Further, the composite index will be converted into the
probability of a crisis to improve the interpretation. Attempting to evaluate the
performance of the model in predicting both in-sample and out-of-sample
currency crises will be the final step.
This chapter is focused on the development of the signal model in predicting
the crisis and the first step is to determine the period of currency crises in
53
Indonesia. However this step is already done in previous chapter, and then this
section focuses on selecting the set of leading indicators, which will be used by
this model in predicting crises.
4.3.1. Selecting Leading Indicators
After determining the crisis variable, the next issue that needs to be addressed
is the identification of the potential leading indicators. The choice of the
indicators is based on theoretical considerations and the availability of high
frequency data, such as monthly data. Knowledge of the sources of currency
crisis provides the basis for identifying possible indicators that will be useful for
developing a model to predict a crisis. According to Kaminsky et al. (1998), an
effective EWS model should include a broad variety of indicators because
currency crises are commonly preceded by multiple economic and sometimes
political problems. Unlike the probit/logit method which is a multivariate
model and can only accommodate a limited number of explanatory variables in
order to avoid multicolinearity (Zhuang and Dowling, 2002), the signal model
is univariate and ignores the correlation amongst independent variables
(Eliasson and Kreuter, 2001).
There are three steps in selecting a set of leading indicators to predict currency
crises. The first step is to generate the signal for each indicator; the second step
is to classify these signals based on their performance to predict a crisis within
prediction horizon; and the final step is to evaluate and select these indicators
using specific selection methods.
The signal model is based on a large number of main economic and financial
variables, which tend to exhibit abnormal behavior before the onset of a crisis.
The abnormal behaviour of one or more leading indicators represents a
warning signal about the possible currency crisis within a specific period of
time. In generating the signals of a potential crisis, the behavior of all indicators,
xit, are transformed into a binary definition of a crisis signal as a “signal” (sit=1),
or “no signal” (sit=0). In empirical work, these indicators demonstrate signals of
54
a potential crisis whenever they depart from their thresholds, because the
thresholds or critical values compartmentalize the distribution of indicators into
a region that is considered as normal from the region that is regarded as
aberrant (Goldstein et al. 2000). If the observed outcome for a particular
variable falls into the abnormal region, the variable in question will send a
warning signal of the presence of a crisis.
Basically the threshold level is chosen on an arbitrary basis in order to
maximize the performance of the indicator when predicting a crisis.
Nevertheless, in determining the value of the threshold, one thing has to be
considered; that is, there is a tradeoff between the risk of having too many false
signals and the risk of missing some crisis exposures. For example, choosing too
low a threshold would result in prompting a number of false signals (noise)
while reducing the number of missed signals. On the contrary, if too high a
threshold is selected, it can reduce the number of false alarms, although at the
risk of missing many signals. Ideally, the choice is to find a balance between
these two sorts of errors.
After generating the signals for each indicator, the next step is to classify these
signals based on their capability to predict a crisis for a specific time horizon or
signaling window (w). Kaminsky et al. (1998) define the time horizon, or
signaling window, or crisis window, as the period during which the indicator
being assessed is expected to display an ability to predict crises. The choice of
the prediction range is arbitrary, and depends on the objectives of the user. For
example, policy makers and public sector movers adopt a relatively long
horizon so that time is be allowed for policy changes that may prevent the
crisis. However, the time horizon of private sector models is shorter and in this
situation, the criterion for evaluation of the accuracy of predictions (frequently,
a trading rule) is sometimes different (Berg et al., 2003). Most previous
researchers have preferred to utilize 24 months for their prediction horizon.
55
Using a two by two matrix in Table 4.1, the signal of each indicator can be
divided into four categories depending on its capability to predict the crisis
within the prediction horizon or signaling window.
TABLE 4.1 Performance Matrix of Early Warning Indicator
Within Signaling Horizon (w)
Crisis (cct=1) No Crisis (cct =0)
Signal (sit=1) A B
No signal (sit=0) C D
In Table 4.1, the first row of the 2x2 matrix indicates that the indicator sends a
signal (sit=1) as it exceeds its threshold. There are two possible categories, the
first, if the indicator issues a signal (sit=1) and a crisis happens within the
signaling horizon (cct=1), then this can be categorized as a “good signal” (cell
A); and the second, if the indicator sends a signal (sit=1) but no crisis occurs
within the crisis window (cct=0), it can be classified as a “false signal” (cell B).
On the other hand, the second row of a 2x2 matrix in Table 4.1 indicates that
even though the indicator failed to send a signal (sit=0) within its prediction
horizon, as it does not pass its threshold, there remain two possible categories:
the first category, if the indicator does not send a signal, but a crisis occurs
within the crisis window which can be considered as a “missed signal” (cell C).
In the other category, if the indicator does not send a signal and no crisis occurs
within the crisis window, it is called a “good silent signal” (cell D).
The signal model selects the set of independent variables based on their
performance in predicting past crises. The performance of an indicator in
predicting a crisis can be shown in the value of its noise-to-signal ratio (NSR).
Basically, the NSR is based on the ability of an indicator to send more good
signals, while at the same time eschewing bad signals. This ratio can be
obtained by taking the ratio of the percentage of bad signals over the
percentage of good signals (Kaminsky et al. 1998) or
56
���� = � ����� + ��� � �
� � + ����� ������������������������������������������4.1�
Correspondingly, the lower value of NSR reflects a more powerful leading
indicator in predicting crises. Therefore, based on this equation, if its NSR is
equal to or higher than one (NSR ≥ 1), its means that this indicator sends
excessive noise because its percentage of bad signals is equal to, or much bigger
than its percentage of good signals. As a result, it contributes less in predicting
crises compared to an indicator with low NSR. Kaminsky (1999) also argues
that the NSR can be used to select which indicators to use when constructing
the composite index.
4.3.2. Constructing a Composite Index
In the previous subsection it was noted that the signal model is a univariate
model, so that after selecting a set of leading indicators, the next step is to
integrate them into one single indicator or composite index. The purpose of
developing a composite index of a currency crisis is to aggregate the “best”
indicator (Goldstein et al., 2000; Krznar, 2004). By combining them into a well-
constructed composite index, the noise of these indicators can be reduced and
the composite index made smoother and more reliable for predicting a crisis.
With these points in mind, a combination of these selected indicators is
converted into one composite index by taking into account their performance,
placing more weight on the best indicators. In this respect, when constructing a
composite index (CIt), Kaminsky (1999) and Goldstein et al. (2000) weighted all
the signals (sit) of all leading indicators by the inverse of its NSR:
��� = � �������
�
�������������������������������������������������������������4.2��
Therefore, in constructing this composite index, the indicators with low NSR
receive more weight compared to those with high NSR.
57
4.3.3. Generating the Probability of a Currency Crisis
The composite index described above cannot be employed to predict a crisis
because it is unable to determine how big a chance a country has of
experiencing a crisis within 24 months. It can, only infer the likelihood that a
country will experience a crisis, for the higher its value, the more likely a
country will be beset with a crisis. So, in order to predict a crisis, this composite
index needs to be transformed into the probability of a crisis. For this purpose,
the composite index is then classified into several intervals using the decile
method. Furthermore, the probability of a crisis for each interval of the
composite index can be calculated using the following formula1:
����� � �!��"� < ��� < ��$�% = &'. '(�)'&*ℎ��, *ℎ���"� < ��� < ��$��-&.�-�/� � ��('00', &1�, *ℎ &�,�)'&*ℎ���&'. '(�)'&*ℎ��, *ℎ���"� < ��� < ��$�
��������4.3�
Where
Pg(Crisis│CIL < CIt < CIU) : The Probability of a crisis for CI ∈ g;
g : the number of intervals
Furthermore, by using the results from the above equation, the composite index
will be converted into the probability of a crisis according to the range
encompassing the composite index values.
4.3.4. Model Performance Evaluation
Unlike the parametric model, that is the probit/logit model, the forecasting
result of the signal model does not display if it is statistically significant or not,
because it does not involve hypothesis testing. Nonetheless, the predictive
power of the signal method can be evaluated in respect to its accuracy and
calibration. In evaluating the forecasting results of this model, this study applies
the Diebold and Rudebusch’s (1989) quadratic probability score (QPS) and
1 This formula is used by Kaminsky (1999); Berg and Pattillo (1999b); Edison (2000)); Zhuang and Dowling (2002); Yap (1998); Knedlik (2006)
58
global score bias (GSB) to evaluate the model’s performance in terms of its
accuracy and calibration2.
These methods allow evaluation of the average closeness between the
occurrence of a crisis and the prediction of a crisis during the pre-crisis
window. The occurrence crisis is determined using Equation 3.2 in Chapter 3.
Recall that this is a one-zero dummy model were the predicted crisis (Pt) is 1 for
“crisis” or 0 for “no crisis” and is defined if the model’s probability of a crisis
passes its threshold, that is, its specific cut-off probability. The accuracy of the
model can be assessed using the following formula:
3�� = 14 � 2��� − ���6
7
�������������������������������������������4.4�
Where Pt : the predicted crisis periods with “1” for crisis and “0” for no
crisis;
Rt : the actual crises periods with “1” for crisis and “0” for no
crisis;
T : the sample period
GSB evaluates the closeness of the mean of the model’s forecasting probability
to the observed relative frequencies (Diebold and Rudebusch, 1989, Kaminsky,
1999) using the following formula:
8�� = 2��9 − �9�6���������������������������������������������������������4.5�
where
�9 = 14 � ��
7
�����������������������������������������������������������������4.6�
2 According to Diebold and Rudebusch (1989), the accuracy refers to the average closeness of predicted probabilities with the realization, as measured by a zero-one dummy variable. Similarly, the calibration is the closeness of predicted probabilities to observed relative frequencies (Zarnowitz, 1992).
59
�9 = 14 � ��
7
������������������������������������������������������������������4.7�
The score of both QPS and GSB ranges from 0 to 2 where a value of 0 represents
perfect accuracy or calibration and two represent perfect failure. This is because
when the model’s prediction and the actual value are the same, there is no
difference between them, or their gap is zero, or Pt = Rt. On the other hand,
when the model totally fails to predict the actual crisis periods, the difference
between them is 1 or -1. In the above equations, the square of this value keeps it
as a positive one. When multiplied by two and the predicted model totally fails
to predict the actual crises, the value of QPS or GSB is two. In other words, its
value being equal to two corresponds to “totally fail to predict”.
According to Berg and Pattillo (1999a), in addition to the above evaluation
method, the performance of the signal model can also be evaluated in terms of:
the percentage observation correctly called; the percentage of pre-crises period
correctly called; and the percentage of tranquil period correctly called. Using
the 2x2 performance matrix in Table 4.1, the crisis signal can be classified into
four categories, such as “A”, “B”, “C” and “D”. The performance of this model
can then be evaluated using the following assessment methods:
The percentage of observations correctly called = (A+D)/(A+B+C+D) (4.8)
The percentage of pre-crisis periods correctly called = A/(A+C) (4.9)
The percentage of tranquil periods correctly called = B/(B+D) (4.10)
The percentage of false alarms of total alarms = B/(A+B) (4.15)
Basically, for the first three measures and unlike the last measure, the higher
these values the better the performance of the model. In contrast, using the last
measure, the lower this ratio, the better the indicator will be.
60
4.4. The Application of General Signal EWS Model for Predicting
Indonesian Currency Crises
4.4.1. Constructing the Signal EWS Model
After explaining the EWS method, this section applies the EWS model based on
the signal model for predicting a currency crisis occurring in Indonesia. For this
purpose, in constructing the signal EWS model, the sample period chosen
ranges from January 1970 to September 2008. As the main purpose of this study
is to develop an EWS model that can predict the Asian financial crisis in
1997/98 within 24 months, the data will be divided into two sub-samples, these
being in-sample and out-of-sample. The in-sample extends from January 1970
to December 1995, and will be used for model building. The out-of-sample goes
from January 1996 to September 2008, and will be used to test the performance
of the EWS model in predicting the Asian Financial Crisis in 1997/98.
Since the determination of the period of currency crises in Indonesia has been
done in previous chapter (Chapter 3), in this section, to construct the Signal
EWS model begins by selecting the set of leading indicators for constructing the
composite index. In common with Kaminsky et al. (1998) and Eliasson and
Kreuter (2001) who used a broader set of leading indicators, this study uses 55
leading indicators that are classified into six categories: the capital account, the
current account, the financial sector, the fiscal sector, the real sector, and the
global economy, as presented in Table A4.1.
As a first step, the behaviour of these 55 monthly leading indicators is
transformed into a binary signal, which is “1” for “signal” if its value passes its
threshold,3 or otherwise, “0” for “no signal”. All these signals from the 55
indicators are then classified according to their ability to predict the in-sample
currency crises (November 1978, April 1983 and September 1986) within the 24-
3 Similar to calculating the thresholds for EMPI in Equation 3.2, the threshold for each leading indicator
is calculated using mean +/- specific number of standard deviation. The value of this specific number
varies across the leading indicators and is adjusted individually until its NSR reaches a minimum level.
61
month crisis window, with reference to the 2x2 performance matrix in Table 4.1.
These leading indicators are then evaluated individually and ranked on their
ability to send more good signals, at the same time the results of keeping on
sending weaker signals using NSR are presented in Table 4.2. Based on this
assessment, ten indicators were found with NSR equal to or greater than unity
(NSR ≥ 1), and six indicators with no NSR, as they failed to send out any good
signals. Following Kaminsky (1999), these indicators were then dropped from
the model because they sent more noise, or played a lesser part in predicting a
crisis. Consequently, only 39 of 55 leading indicators are used to construct a
composite index.
TABLE 4.2 The Performance Evaluation of Individual Indicators
No Leading Indicators A/(A+C) B/(B+D) NSR
01 Real US$/yen exchange rate1 14.47% 0.43% 0.03
02 Short-term capital flows to GDP 13.16% 0.45% 0.03
03 Current account balance to GDP 21.15% 0.79% 0.04
04 US annual growth rate 14.47% 0.85% 0.06
05 US real interest rate3 5.26% 0.45% 0.09
06 Short-term capital flows to GDP3 3.95% 0.47% 0.12
07 US real interest rate 22.37% 2.98% 0.13
08 Loans to deposits3 15.79% 3.59% 0.23
09 M1 to GDP3 11.84% 2.69% 0.23
10 Real effective exchange rate1 59.21% 14.04% 0.24
11 Domestic real interest rate3 100.00% 26.47% 0.26
12 Exports2 28.95% 8.52% 0.29
13 M1 to GDP 6.58% 2.13% 0.32
14 Government consumption to GDP 85.53% 28.51% 0.33
15 Foreign reserves in months of imports 2.63% 0.89% 0.34
16 Trade balance to GDP3 6.58% 2.24% 0.34
17 Foreign reserves2 15.79% 5.38% 0.34
18 Foreign reserves in months of imports3 26.32% 9.43% 0.36
19 Government consumption to GDP, 12 m change 63.16% 23.77% 0.38
20 Domestic real interest rate differential from US rate3
75.00% 28.43% 0.38
21 Lending ]deposit rate spread 54.55% 21.50% 0.39
22 Current account balance to GDP3 16.28% 6.45% 0.40
23 Deposits to M23 7.89% 3.14% 0.40
24 Net credit to government to GDP3 26.92% 11.02% 0.41
25 Fiscal balance to GDP3 42.11% 17.94% 0.43
26 Deposits in BIS banks to reserves3 100.00% 44.44% 0.44
27 Real exchange rate against US$1 30.26% 15.74% 0.52
28 Domestic real interest rate 36.36% 20.56% 0.57
62
TABLE 4.2 The Performance Evaluation of Individual Indicators (Continued)
No Leading Indicators A/(A+C) B/(B+D) NSR
29 M2 to reserves3 13.16% 7.62% 0.58
30 Fiscal balance to GDP 27.63% 16.60% 0.60
31 M2 multiplier2 65.79% 40.81% 0.62
32 M2 multiplier 48.68% 30.21% 0.62
33 Oil price 82.89% 51.49% 0.62
34 M2 to reserves 1.32% 0.85% 0.65
35 Domestic credit to GDP3 39.47% 28.70% 0.73
36 Central bank credit to the public sector to GDP 31.58% 23.40% 0.74
37 Domestic real interest rate differential from US rate
27.27% 22.43% 0.82
38 Real commercial bank deposits2 76.32% 72.20% 0.95
39 Trade balance to GDP 35.53% 35.32% 0.99
40 Short-term external debt to reserves3 52.63% 52.47% 1.00
41 Foreign liabilities to foreign assets3 44.74% 58.30% 1.30
42 Domestic credit to GDP 57.89% 78.30% 1.35
43 Net credit to government to GDP 50.00% 82.01% 1.64
44 Central bank credit to the public sector to GDP3 13.16% 21.97% 1.67
45 Short-term external debt to reserves 5.26% 8.94% 1.70
46 Imports2 13.16% 24.22% 1.84
47 Stock price index in local currency2 25.00% 48.67% 1.95
48 Foreign liabilities to foreign assets 5.26% 20.85% 3.96
49 Loans to deposits 5.26% 32.34% 6.14
50 Deposits in BIS banks to reserves 0.00% 9.09% NA
51 Deposits to M2 0.00% 1.28% NA
52 Lending-deposit rate spread3 0.00% 42.16% NA
53 Oil price2 0.00% 2.69% NA
54 Industrial/manufacturing production index2 0.00% 0.00% NA
55 Domestic consumer price index2 0.00% 12.11% NA
Note: 1 deviation from trend-HP filter, 2 12 months percentage change, 3 12 months change, NA not available.
The next step is to integrate all signals from these 39 indicators into one
composite index. According to Kaminsky (1999), the composite index can be
obtained by summing up all signals from these indicators. This method
assumes that the predictive power is the same over all the indicators. In fact,
based on the value of NSR, which is listed in Table 4.2, the predictive power of
each indicator is different. In respect to this situation, Kaminsky (1999) and
Goldstein et al. (2000) propose an alternative way to construct the composite
index by employing a weight of the inverse of the minimum adjusted noise-to-
signal-ratio on each signal mentioned in Equation 4.2. This means that
indicators with the lower NSR will contribute more to develop a composite
63
index compared to indicators with a higher noise-to-signal-ratio. For example,
the real US$/yen exchange rate, with NSR of 0.03, based on the calculation of
Equation 4.2, has a weight of 33.33. In contrast, the trade balance to GDP of the
NSR is 0.99, and using the same method, it will be given a weight of 1.01. The
time-series composite index is presented in Figure 4.1.
FIGURE 4.1 Composites Index, 1970-2008
The composite index can only infer the likelihood that a country will experience
a crisis, as the higher its value, the more likely a country will be beset with a
crisis. In this study, the value of the composite index ranges from 0 to 120.6;
therefore it is difficult to interpret the meaning of an individual value in the
composite index. To attempt to clarify this situation, the composite index is
converted to a measure of the probability of a crisis occurring within the next 24
months. For this purpose, the composite index is classified into ten intervals
using the decile method; however, based on the calculations, it was found that
several intervals had the same lower and upper bounds - thus this study used
only eight intervals. The probability of a crisis for each interval of the composite
index can then be calculated using Equation 4.3, the result of which is shown in
Table 4.3.
0
25
50
75
100
125
Jan-70
Jan-71
Jan-72
Jan-73
Jan-74
Jan-75
Jan-76
Jan-77
Jan-78
Jan-79
Jan-80
Jan-81
Jan-82
Jan-83
Jan-84
Jan-85
Jan-86
Jan-87
Jan-88
Jan-89
Jan-90
Jan-91
Jan-92
Jan-93
Jan-94
Jan-95
Jan-96
Jan-97
Jan-98
Jan-99
Jan-00
Jan-01
Jan-02
Jan-03
Jan-04
Jan-05
Jan-06
Jan-07
Jan-08
64
TABLE 4.3 Composite Index and Probabilities of a Currency Crisis
No Deciles Range of Composite Index Probability of a crisis
1 0 - 2nd 0.0 < CI ≤ 7.9 0%
2 2nd – 4th 7.9 < CI ≤ 11.6 3.2%
3 4th – 5th 11.6 < CI ≤ 14.0 6.5%
4 5th – 6th 14.0 < CI ≤ 16.8 12.5%
5 6th – 7th 16.8 < CI ≤ 19.5 35.5%
6 7th – 8th 19.5 < CI ≤ 23.9 37.5%
7 8th – 9th 23.9 < CI ≤ 33.1 51.7%
8 9th - 10th 33.1 < CI ≤ 120.6 83.9%
With reference to Table 4.3, each composite index for all samples periods is
converted into the probability of a crisis that corresponds according to which
interval of composite index it falls. For example, if the composite index at time t
is 5, then based on the above table, it will go into the first interval and will be
converted to the probability of a crisis with the value of 0%. However, if the
value of the composite index at time t is equal to 20, then it falls into the sixth
interval and the probability of a crisis becomes 37.5%. The time-series
probability of a crisis is presented in Figures 4.2 and 4.3.
4.4.2. Predicting Indonesian Currency Crises
In this subsection, this signal model is used to predict and evaluate its ability to
predict the Indonesian currency crises, as presented in Figure 4.2 for the in-
sample prediction (1970-1995), and Figure 4.3 for the out-of-sample prediction
(1996-2008). In these figures, the red solid line is the probability of a crisis. As
already mentioned in the previous chapter, Indonesia experienced four
episodes of currency crisis, being three during the in-sample period 1970-1995
(November 1978, April 1983 and September 1986), and one currency crisis
episode in the out-of-sample period 1996-1998, that being the Asian financial
crisis of 1997/98. As this study uses the 24-month of crisis window, the yellow
shaded areas (cc_24m) represent the 24 months prior to the currency crises.
65
In-Sample Prediction (1970-1995)
Using Figure 4.2 to predict the three in-sample currency crises between 1970–
1995, it was found that this model was able to predict all three because the
probability of a crisis tended to increase within 24 months prior to these events.
For example, in predicting the first episode of a currency crisis in Indonesia that
occurred in November 1978, the model sent signals about the potential currency
crisis from December 1976, when the probability of a crisis was about 36%. This
probability increased to 52% in January 1977 and jumped to 84% in January
1978 where it remained until the crisis happened in November 1978.
In the second in-sample crisis that happened in April 1983, the model started by
sending a signal from May 1981 when the probability of a crisis happening
within 24 months was about 52%. This continued to increase to 84% in June
1981, or almost 19 months prior to the crisis. Unlike the first two crisis episodes,
in the third crisis that happened in September 1986, the first signal for the
occurrence appeared in October 1984 at about 13%, and then increased to 84%
from January to March 1985, before declining to 38% in June 1985. But in March
1986, or six months prior to the crisis, the probability of a crisis jumped to 52%.
Although this model was able to capture all the in-sample currency crises in
Indonesia, it also sent some false alarms, especially in the early 1970s and 1990s.
For example, the model’s probability of a crisis increased from 13% in January
1973 to 52% in January 1974. One year later, the model indicated that Indonesia
would be hit by a crisis as the probability of a crisis increased from 36% in
January 1975 to 52% in March 1975 and remained high until December 1975. In
the early 1990s, the model’s probability of a crisis tended to fluctuate but
reached a peak of 52% in November 1992.
FIGURE 4.2 The General Signal Model: I
During these periods Indonesia also experienced several major ev
the large demonstration of students
Kukui Tanaka, Prime Minister of Japan
anti-Japan/China riots known as “Malari” (Indonesian acronym for Malapetaka
lima belas Januari, or 15 January’s disaster). This riot
government to apply new foreign capital regulation to localize the ownership of
foreign companies and to restrict
(Sato, 2003). In addition, at th
between Indonesia and East Timor (now, Timor Leste). Moreover,
1992, there was an episode involving a
liquidated Bank Summa International, one of the te
Indonesia.
Out-of-Sample Prediction (1996
To test the performance of this signal
currency crises, Figure 4.3 shows
to 2008. This model is evaluated
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%1
97
0M
01
19
70
M1
2
19
71
M1
1
19
72
M1
0
19
73
M0
9
19
74
M0
8
19
75
M0
7
19
76
M0
6
19
77
M0
5
19
78
M0
4
66
eneral Signal Model: In-Sample Prediction
Indonesia also experienced several major events, such as
the large demonstration of students that took place on arrival in Jakarta
Kukui Tanaka, Prime Minister of Japan on 15 January 1974, which sparked the
known as “Malari” (Indonesian acronym for Malapetaka
or 15 January’s disaster). This riot was a turning point for
government to apply new foreign capital regulation to localize the ownership of
foreign companies and to restrict the use of foreign employees in Indonesia
the end of 1975, there was a military confrontation
between Indonesia and East Timor (now, Timor Leste). Moreover, at the end of
involving a “mini banking crisis” as Bank Indonesia
liquidated Bank Summa International, one of the ten largest private
(1996-2008)
To test the performance of this signal model in predicting the out-of
shows the time series probability of a crisis from 1996
model is evaluated here in terms of its ability to predict the
19
78
M0
4
19
79
M0
3
19
80
M0
2
19
81
M0
1
19
81
M1
2
19
82
M1
1
19
83
M1
0
19
84
M0
9
19
85
M0
8
19
86
M0
7
19
87
M0
6
19
88
M0
5
19
89
M0
4
19
90
M0
3
19
91
M0
2
19
92
M0
1
19
92
M1
2
cc24m signal
ents, such as
in Jakarta of Mr.
which sparked the
known as “Malari” (Indonesian acronym for Malapetaka
a turning point for the
government to apply new foreign capital regulation to localize the ownership of
in Indonesia
e end of 1975, there was a military confrontation
the end of
Bank Indonesia
private banks in
of-sample
from 1996
of its ability to predict the
19
92
M1
2
19
93
M1
1
19
94
M1
0
19
95
M0
9
1997/98 Asian financial crisis
this period.
FIGURE 4.
Based on this figure, warning signals
from April 1996 when
crisis was about 36%
before dropping to 3
increased significantly and reach
seven months prior to the
financial crisis in August 1997
decline to 7% in July
remaining high for
probability of a crisis decline significantly to 38%
September 1998.
To see whether this model could capture any potential risks that occurred in the
Indonesian economy during this period
January 1999 to September 2008. As seen in Figure 4.
signal model sent
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%1
99
6M
01
19
96
M0
7
19
97
M0
1
19
97
M0
7
19
98
M0
1
67
1997/98 Asian financial crisis, this being the only currency crisis found during
FIGURE 4.3 The General Signal Model: Out-of-Sample
Based on this figure, warning signals began to show the presence of this crisis
when the probability of Indonesia experiencing a
about 36%. This figure increased to 38% in the following
to 3% in August 1996. After this, the probabilit
significantly and reached a high of 84% in January 1997
seven months prior to the first time EMPI crossed its threshold for the Asian
in August 1997. After that the probability of
July 1997, before increasing again to 84% in January 1998
high for another three months. However, after
crisis decline significantly to 38% and continue
To see whether this model could capture any potential risks that occurred in the
Indonesian economy during this period the sample period
January 1999 to September 2008. As seen in Figure 4.3, during this period
some warning signals that indicated Indonesia would be
19
98
M0
1
19
98
M0
7
19
99
M0
1
19
99
M0
7
20
00
M0
1
20
00
M0
7
20
01
M0
1
20
01
M0
7
20
02
M0
1
20
02
M0
7
20
03
M0
1
20
03
M0
7
20
04
M0
1
20
04
M0
7
20
05
M0
1
20
05
M0
7
cc24m signal
the only currency crisis found during
ample Prediction
presence of this crisis
experiencing a currency
to 38% in the following month
the probability of a crisis
in January 1997, that is, some
its threshold for the Asian
probability of a crisis tended to
84% in January 1998,
after April 1998, the
and continued drop to 13% in
To see whether this model could capture any potential risks that occurred in the
the sample period was extended from
during this period, the
warning signals that indicated Indonesia would be
20
05
M0
7
20
06
M0
1
20
06
M0
7
20
07
M0
1
20
07
M0
7
20
08
M0
1
20
08
M0
7
68
plagued by crises within the 24-month crisis window. For example, the model
started to send warning signals from January 1999 with the probability of a
crisis being 35.5%. This continued to increase to 52% in June 1999, which
indicates that the chance of Indonesia experiencing a crisis within 24 months
onwards was about 52%. The model’s probability of a crisis remained at 52%
until March 2000 before decreasing to 12.5%. However, the probability of a
crisis increased again to 52% in January 2001 and peaked at 84% in August
2001.
After October 2003, the model began to send warning signals with an average
probability of around 35%, and finally, warning signals from January 2008
indicated the probability of a currency crisis happening within 24 months was
52%. The crisis probability remained high to peak at 84% in September 2008.
However, as the purpose of this study is to develop the EWS model to predict
currency crises, and as the only out-of-sample currency crisis was the Asian
Financial Crisis in 1997/98, these warning signals can be seen as false alarms or
false signals.
In fact, Indonesia experienced several vulnerabilities during the period from
1999 to 2008, and significantly these events occurred within 24 months after the
model’s false alarms. For example, after the first alarm in the out-of-sample
period, there was political instability that led the People's Consultative
Assembly (Majelis Permusyawaratan Rakyat, MPR) to impeach the fourth
President of Republic of Indonesia, Abdurrahman Wahid. Then on 23 July 2001,
Wahid was replaced by Megawati Sukarnoputri as the fifth President. In
relation to the second false alarm, within 24 months following the first signal in
October 2003, Indonesia experienced economic problems triggered by increases
in the world oil price. This forced the Indonesian government to remove
substantial subsidies on domestic fuel in May and October 2005. As a result, the
domestic oil price climbed significantly and the inflation rate increased to 17%.
Lastly, associated with the third false alarm, which started in January 2008,
there was a global financial crisis that was derived from the sub-prime
69
mortgage crisis in the United States of America, which in turn affected many
countries around the world, including Indonesia.
4.4.3 The Signal Model’s Performance Evaluation
In evaluating the reliability of the model in predicting these above crisis
episodes, as mentioned earlier that the probability of a crisis need to be
converted into one-zero dummy model predicted crises using cut-off
probabilities as the threshold. Following that, this study sets up four cut-off
probabilities (Pr*), namely 20%, 30%, 40% and 50%. It means that the model will
indicate a currency crisis if the probability of a crisis crosses these cut-off
probabilities. The in-sample performance is evaluated based on the ability of
the model to predict the three in-sample currency crises, while the out-of-
sample performance evaluation is based on the ability to predict the 1997/98
Asian financial crisis. Details of the model’s performance evaluation for both in-
sample and out-of-sample currency crises can be seen in Table 4.4.
With the cut-off probability of 20% and 30%, the model correctly figures out the
overall crisis episodes at about 83% for in-sample crises and 73% for the out-of-
sample crises. However, when Pr* increases to 50%, the ability of the models to
predict 24 months prior to the crisis fell to 57% (in-sample) and 30% (out-of-
sample). In contrast, in capturing the tranquil periods, this model performs well
in line with the increase in Pr*. For example, at Pr*=20%, this model correctly
captures the tranquil period by 73% (in-sample) and 36% (out-of-sample),
however, as Pr* increases to 50%, the ability of this model also increases to 92%
and to 76%, respectively.
Generally, the performance of this signal model can also be seen from the
ability of the model to capture all observation including crisis and tranquil
periods. As shown in Table 4.4, at Pr*=20%, this model can capture 77% (in-
sample) and 43% (out-of-sample). The ability of this model increases in line
with increasing Pr*, for example when Pr*=50%, its performance increases to
83% (in-sample) and 67% (out-of-sample). In contrast, as Pr* increases the ability
70
of the model to predict the whole observation during the crisis period from
1996 to 1998 drops, so that at Pr*=20%, this model captures the whole period by
78%, although it declines to 42% as Pr* increases to 50%. Moreover, in
predicting these currency crises, the performance of this model can also be
evaluated in term of its accuracy and calibration using the ratio of QPS and
GSB, as mentioned in Table 4.4. According to these measurements, in general,
this model performs better than is indicated by its low QPS and GSB for both
in-sample and out-of-sample.
TABLE 4.4 The General Signal Model’s Performance Evaluations
Thresholds (Pr*)
Assessment methods In-sample Out-of-sample
1970-1995 1996-1998 1996-2008
20%
% of observation correctly called 76.53% 77.78% 43.14%
% of pre-crisis periods correctly called 82.90% 73.33% 73.33%
% of tranquil periods correctly called 74.47% 100.00% 35.77%
% of false alarms of total alarms 48.78% 0.00% 78.22%
QPS 0.4695 0.4444 1.1373
GSB 0.0457 0.0988 0.4307
30%
% of observation correctly called 76.53% 77.78% 43.14%
% of pre-crisis periods correctly called 82.90% 73.33% 73.33%
% of tranquil periods correctly called 74.47% 100.00% 35.77%
% of false alarms of total alarms 48.78% 0.00% 78.22%
QPS 0.4695 0.4444 1.1373
GSB 0.0457 0.0988 0.4307
40%
% of observation correctly called 83.28% 41.67% 66.67%
% of pre-crisis periods correctly called 56.58% 30.00% 30.00%
% of tranquil periods correctly called 91.92% 100.00% 75.61%
% of false alarms of total alarms 30.65% 0.00% 76.92%
QPS 0.3344 1.1667 0.6667
GSB 0.0041 0.6806 0.0069
50%
% of observation correctly called 83.28% 41.67% 66.67%
% of pre-crisis periods correctly called 56.58% 30.00% 30.00%
% of tranquil periods correctly called 91.92% 100.00% 75.61%
% of false alarms of total alarms 30.65% 0.00% 76.92%
QPS 0.3344 1.1667 0.6667
GSB 0.0041 0.6806 0.0069
As noted in the previous subsection, based on Figures 4.2 and 4.3 this model
sends lots of false alarms for both in-samples and out-of-samples. It is also
supported by the percentage of false signals relative to the total signals in Table
4.4. According to this measure, during the period 1970 to 1995, this model sent
71
49% of false alarms at Pr*=20%. However as Pr*is increased, the number of false
alarms tended to decline, for example, at Pr*=50%, the number of in-sample
false alarms declines to 31%. However, during the crisis period from 1996 to
1998, the model sent no false alarms at all levels of cut-off probabilities. In
contrast, for the entire out-of-sample period from 1996 to 2008, this model sent
many false alarms, for example, at Pr*=20% it sent 78% of false alarms, as Pr*
increased to 50%, its percentage of false alarms relative to total signals dropped
slightly to 77%.
4.5. Assessing Sector Specific Forecasting and the Crisis Channels
In the previous section it was pointed out that this study uses an extensive data
set representing 55 indicators. These indicators can be classified into six sectors,
namely the capital account, the current account, the financial sector, the fiscal
account, the global economy and the real sector. In this section, the analysis is
extended by employing the sector-specific signal model to identify which sector
is the most useful for forecasting currency vulnerability and explaining the
source of these currency crises. For this purpose, using the same method and
assumptions as the general model, the probability of a crisis based on
modification from each sector is calculated. In addition, to evaluate the
performance of these sector-specific models, the 30% cut-off probability is used,
as the previous section indicates that this threshold is the optimal cut-off
probability which performs very well with the general model in predicting both
in-sample and out-of-sample Indonesian currency crises.
4.5.1. Capital Account Sector Specific Signal EWS Model
In developing the sectoral model for the capital account, this sector used 11
from 15 leading indicators. Three indicators have their NSR greater than unity,
while the other indicators have no NSR due to the inability to send a good
signal, as can be seen in Table A4.2. Using the same approach with the general
model, the probability of a crisis for this sector is calculated, and Figure 4.4
72
presents the time-series of the probability of a crisis for both in-sample and out-
of-sample periods.
In that figure, the probability of a crisis varies from 0 to 77%. For the in-sample
performance this sector was able to predict two of three in-sample crises as
precisely November 1978 and April 1983, but it was unable to predict the third
in-sample crisis in September 1986. The first indication for the crisis of 1978 is
seen in January 1978 when the probability of a crisis reached 77%, where it
remained until the crisis occurred. In the second crisis episode, during the first
24 months prior to the crisis of May 1981, the indication for a crisis to occur was
about 20%. This tended to increase closer to the crisis date and reached 77% in
June 1982, or ten months before the crisis took place in April 1983.
FIGURE 4.4 Probability of a Crisis for the Capital Account, 1970-2008
In contrast, this sector failed to predict the last in-sample crisis episode in
September 1986 because its probability of a crisis only reached 20% on the first
two of the 24-months crisis windows. After this it disappeared, returning again
to 20% in December 1985 where it remained until the crisis happened in
September 1986. Moreover, during this period, it also generated several false
signals particularly in the early 1970s and 1990s.
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
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cc_24m KA
73
At the outset, the out-of-sample performance in Figure 4.4 shows that this
sector sent weak alarm signals about the occurrence of the Asian financial crisis,
with the crisis probability of about 7% in March 1996, increasing to 16% in
August 1996 before shifting back to 0% in January 1997. After that, its crisis
probability fluctuates around 0% to 16%; however, after the Indonesian
government closed 16 small banks in November 1997, the probability of a crisis
jumped to 77% in December 1997 and remained there consistently until March
1998, before moving back to 7% in May 1998 where it remained until the end of
crisis window.
As the sample period was extended, this sector still sent warning signals, as its
probability of a crisis increased during this period. For example, a significant
alarm occurred in August 2001, as well as from July to October 2005, when its
probability of a crisis was about 77%. Indonesia also experienced two major
events during this period, namely the impeachment of the fourth President of
Republic of Indonesia in July 2001, and in May and October 2005, the
Indonesian government implemented the policy on substantial removal of the
oil subsidy by increasing the domestic oil price. However, as the purpose of this
model is to predict currency crises, these warning signals can be categorized as
false alarms.
4.5.2. Current Account Sector Specific Signal EWS Model
In constructing its composite index as well as the probability of a crisis, this
sector only uses 7 of 8 indicators as it drops the 12-month change (yoy) of
imports because its NSR is greater than unity; see Table A.4.3. Figure 4.5 shows
that the current account signal model can predict all the crisis episodes, both in-
sample and out-of-sample. In the first crisis in November 1978, warning signals
were received about the possibility of the crisis happening within 24 months.
This was about 46%, a figure that had increased to 73% in November 1977
where it remained for another three months before shifting back to 43%.
74
For the second in-sample crisis episode, the same situation applied, as the
probability of a crisis reached 73% some 24 months prior to this crisis. After
that, it tended to fluctuate with an average probability of about 68%. However,
following January 1983 its probability of a crisis remained at 73% until the date
of the crisis. In the last in-sample evaluation, 24 months prior to the crisis, the
probability of a crisis jumped to 73% before returning to around 46% for the rest
of the period until the crisis time in September 1986, except in June 1985 and
March 1986, when 73% was recorded. However, during this in-sample period,
this sector also sent lots of false alarms, particularly at the beginning of 1970s.
FIGURE 4.5 Probability of a Crisis of the Current Account, 1970-2008
In the out-of-sample, the first indication of Asian financial crisis can be seen in
April 1996 when the probability of a crisis was 46%, a figure that increased to
73% in the following month. After this it tends to fluctuate around that value
before dropping to 0% from July to September 1997, and fluctuates around 0%
to 18% for the rest of crisis window. Moreover, as the sample period has been
extended to 2008, this sector still sends lots of warning signals with the
probability of a crisis reaching 43 to 46% during 1999. A peak at 73% was
reached in June 1999, from 2001 to 2003, and from August to November 2004.
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1/1/
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cc_24m CA
75
Similarly, after January 2006, its probability of a crisis reached 43% until the end
of 2007, before jumping to 73% in 2008. As this study sets 30% as the cut-off
probability, these warning signals indicate that Indonesia would be hit by a
crisis and the probability for Indonesia having a crisis within 24 months in 2008
was 77%. Even though Indonesia experienced some vulnerability in this period,
no currency crisis did occur, thus, the warning signals can be categorized as
false alarms.
4.5.3. Financial Sector Specific Signal EWS Model
This sector only utilizes 11 of 15 potential leading indicators to construct the
composite index because their NSR is less than unity as seen in Table A.4.4. The
sector’s probability of a crisis is presented in Figure 4.6. The in-sample
prediction indicates that the financial sector only sent five significant warning
signals with the probability of a crisis reaching 60% in May 1971, January 1984,
and three times during the 24 months prior to the third in-sample crisis, in
February and August 1985, and January 1986. For the rest of sample periods its
probability of a crisis tended to fluctuate between 0 to 33%, though it sometimes
reached 39%.
FIGURE 4.6 Probability of a Crisis for the Financial Sector, 1970-2008
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1/1/
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cc_24m FS
76
For the first two in-sample crises, warning signals were sent with a 32%
probability of a crisis. Setting the 30% cut-off probability as the threshold to
define the crises, the currency crisis is determined whenever the model’s crisis
probability crosses this threshold. In Figure 4.6 it is shown that this sector is
able to capture all the in-sample currency crises with the probability of a crisis
fluctuating around 33 to 39%. On the other hand, the ability of this model to
capture the tranquil periods is limited, as noted by the large number of false
alarms sent during this period.
Similarly, when predicting the out-of-sample currency crises, warning signals
were sent from January 1996 with the probability of a crisis at 32%, further
increasing to 39% after January 1997 for almost the rest of the crisis window,
before going back to 32% in January 1998. As the sample has been extended to
the end of 2008, this figure shows that this sector sent many false signals during
this period, particularly during the impeachment of Abdurahman Wahid as the
fourth President of Republic of Indonesia in July 2001, during the episode of
mini crisis in 2005, as well as the presence of the Global Financial Crisis in 2008.
4.5.4. Fiscal Account Sector Specific Signal EWS Model
In this section a EWS model using 6 of 8 leading indicators is developed, see
Table A.4.5. The probability of a crisis for this section is presented in Figure 4.7.
Based on this figure, in the first in-sample crisis, warning signals were sent from
48% at the beginning of a 24 month-crisis window to 54% from January 1978
until the crisis date. In the next two in-sample crises, the fiscal account provides
the probability of having a currency crisis of 86%.
On the other hand, in the out-of-sample, there was a failure to predict the Asian
financial crisis, as its probability of a crisis remained lower for the whole a 24
month-crisis window, reaching only 21%, or less than the selected cut-off
probability of 30%. However, the probability of a crisis increased substantially
to 84% in September 1998 for about four months before going back to 54% until
January 2000 and falling even further to 18% in the following month. The crisis
77
probability increased again to 54% from January 2002, and further increased to
86% in August 2003, where it remained for five months. It then dropped to 18%
until the end of sample period, with the exception of 2005 when the probability
of a crisis slightly increased to 21% for the entire year. But, as there was no
currency crisis occurring within 24 months after these warning signals, these
signals can be declared false alarms.
FIGURE 4.7 Probability of a Crisis for the Fiscal Accounts, 1970-2008
4.5.5. Global Economy Sector Specific Signal EWS Model
In developing the EWS model for this sector, this study uses 5 of 6 leading
indicators, with its probability of a crisis being presented in Figure 4.8. Based on
this figure, it was found that this sector was unable to predict the first in-sample
crisis in November 1978, as its crisis probability was only 18% for the whole
crisis window. On the other hand, in the next two in-sample crises, in April
1983 and September 1986, the presence of both crises can be identified, as the
probability of a crisis climbed to 82% within the 24-month crisis window.
However, unlike the second in-sample crisis, this percentage of probability for
the third in-sample crisis dropped substantially to 18% after April 1984 before
0%
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30%
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60%
70%
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90%
100%
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2008
cc_24m FA
78
further dropping to 0% after January 1986. During the in-sample period, less
false signals were sent, particularly in January 1974.
As with predicting the third in-sample crisis, when predicting the out-of-
sample currency crises, the probability results are low for almost the whole
crisis window. Warning signals are noted from March 1996 with probability of
a crisis being 18%, after which there is a jump to 82% from January to February
1997, before fluctuations between 0 to 40% are witnessed in the rest of the crisis
window. As the sample period is extended to September 2008, this sector sends
some false alarms as its crisis probability plummets before reaching to a peak of
82% for the whole of 2001, as well as the whole of 2008.
FIGURE 4.8 Probability of a Crisis for the Global Economy, 1970-2008
4.5.6. Real Sector Specific Signal EWS Model
Unfortunately, for this category, the study is unable to construct either the
composite index or the probability of a crisis because there are no indicators
with NSR less than unity.
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1/1/
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cc_24m GE
79
4.5.7. Performance Evaluation for Sector Specific Forecasting Results
As mentioned earlier in this section, this study uses the 30% cut-off probability
to evaluate the performance of these sector specific signal EWS models, so these
sectors can define currency crises if their probability of a crisis crosses this
threshold. As the focus of this study is to develop an EWS model to predict
currency crises, more attention is placed on the ability of this model to predict
currency crises. However, similar to the assessment of the general model in
Table 4.4 in the previous section, this section also includes the performance
assessment results from the other measurements in Table 4.5.
In terms of the ability to predict the 24 month pre-crisis period, this study found
that the financial sector performed very well, as it was able to capture 99% of
the 24-month in-sample pre-crisis period, followed by the current account, the
fiscal account, the global economy and the capital account, which were able to
capture 80%, 68%, 29% and 26%, respectively. Similarly, in predicting the out-
of-sample crisis, the financial sector also performed better than the other
sectors, as it was able to capture 90% of the pre-crisis period, followed by the
current account, the global economy and the capital account. On the other hand,
unlike the other sectors, the fiscal account failed to predict the out-of-sample
pre-crisis period, as it was unable to send any warning signals during this pre-
crisis period.
In this model, warning signals were generally generated based on their
behaviour before the onset of a crisis. These signals would be sent whenever the
movement of indicators crossed their thresholds. Thus, more vulnerable
indicators would send more signals. Similarly, in terms of sector-specific
signals, more vulnerable sectors would send more signals that would result in a
higher percentage of the pre-crisis period being correctly predicted, but would
reduce the percentage of tranquil periods correctly called because of the high
incidence of false alarms. This study found that compared to other sectors, the
domestic sector, particularly the financial sector, was the most vulnerable
sector. This finding is also supported by Zhuang and Dowling (2002), who
80
found that for Indonesia, domestic weaknesses were the main source of the
Asian financial crisis.
TABLE 4.5 Performance Evaluation of the Sector Specific Signal EWS Models*
Periods Assessment methods KA CA FS FA GE
In-sample
1970-95
% of observations correctly called 80.39% 78.14% 56.59% 82.64% 79.42%
% of pre-crisis periods correctly called 26.32% 80.26% 98.68% 68.42% 28.95%
% of tranquil periods correctly called 97.87% 77.45% 42.98% 87.23% 95.74%
% of false alarms of total alarms 20.00% 46.49% 64.11% 36.59% 31.25%
NSR 0.0489 0.2847 0.5930 0.2013 0.0832
QPS 0.3923 0.4373 0.8682 0.3473 0.4116
GSB 0.0538 0.0299 0.3658 0.0007 0.0400
Out-of-sample
1996-1998
% of observations correctly called 27.78% 55.56% 86.11% 5.56% 41.67%
% of pre-crisis periods correctly called 13.33% 46.67% 90.00% 0.00% 30.00%
% of tranquil periods correctly called 100.00% 100.00% 66.67% 33.33% 100.00%
% of false alarms of total alarms 0.00% 0.00% 6.90% 100.00% 0.00%
NSR 0.0000 0.0000 0.3704 NA 0.0000
QPS 1.4444 0.8889 0.2778 1.8889 1.1667
GSB 1.0432 0.3951 0.0015 1.0432 0.6806
1996-2008
% of observations correctly called 79.08% 34.64% 59.48% 53.59% 72.55%
% of pre-crisis periods correctly called 13.33% 46.67% 90.00% 0.00% 30.00%
% of tranquil periods correctly called 95.12% 31.71% 52.03% 66.67% 82.93%
% of false alarms of total alarms 60.00% 85.71% 68.60% 100.00% 70.00%
NSR 0.0488 0.6829 0.4797 NA 0.1707
QPS 0.4183 1.3072 0.8105 0.9281 0.5490
GSB 0.0342 0.0000 0.2679 0.0103 0.0000
Note: KA: capital account; CA: current account; FS: financial sector; FA: fiscal account; GE: global economy; NA: not available; * based on a 30% cut-off-probability.
In relation to predicting the three in-sample crisis episodes (November 1978,
April 1983 and September 1986); using the 30% cut-off probability, this study
found that only three sectors could predict these crises, namely the current
account, the financial sector and the fiscal sector. The fact that these three
sectors dominated the percentage of pre-crisis periods identified in Table 4.5
could help to support these finding. Moreover, for out-of-sample predictions,
two sectors, the fiscal account and real sector, did not send any warning signals
about the presence of the Asian Financial Crisis in 1997/98. This can be seen in
Figures 4.4 to 4.8, with the following Table 4.6 giving their prediction results for
all currency crises in Indonesia from 1970 to 2008.
81
TABLE 4.6 The Forecasting Results on Indonesian Currency Crises, 1970-2008
The Cut-off Probability is 30 %
Sector
Currency Crisis Period
November 1978
April 1983 September
1986 Asian Financial Crisis
in 1997/98
Overall √√√√ √√√√ √√√√ √√√√
Capital Account √√√√ √√√√ √√√√
Current Account √√√√ √√√√ √√√√ √√√√
Financial Sector √√√√ √√√√ √√√√ √√√√
Fiscal Account √√√√ √√√√ √√√√
Global Economy √√√√ √√√√ √√√√
4.6. Conclusions
This study has attempted to develop an early warning system model to predict
the episode of currency crises in Indonesia from January 1970 to September
2008. For this purpose, this chapter employed a signal approach EWS model to
predict the currency crises using 39 out of 55 monthly leading indicators from
six sectors, namely the capital account, the current account, the financial sector,
the fiscal account, the global economy and the real sector.
The findings indicate that the model correctly captured all crisis episodes for
both in-sample and out-sample periods. In addition, according to the validity
measures, the model correctly predicted 83% (for in-sample crises) and 73% (for
out-of-sample crises) at the 30% cut-off probability. However, the model also
sent many false signals during these periods.
In addition, the model points to domestic sector weaknesses as the main
underlying factor for Indonesian currency crises. In relation to sector specific
analysis, the financial sector is the most dominant sector compared to the other
five sectors. The model is also able to identify the most vulnerable sectors
because sending many false alarms limits its predictions for tranquil periods.
.
82
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res
erv
es
12 m
ch
ang
e T
he
rati
o o
f T
ota
l cl
aim
s o
n B
IS (
BIS
web
site
: lo
cati
on
al b
ank
ing
sta
tisi
tics
. Tab
le 6
A, l
iab
ilit
ies
Lo
cati
on
al)
to F
ore
ign
res
erv
es m
inu
s g
old
(IF
S l
ine
1L.D
)
Do
mes
tic
real
in
tere
st r
ate
dif
fere
nti
al
fro
m U
S r
ate
[L
end
ing
rat
e (I
FS
lin
e 60
P)-
Do
mes
tic
CP
I(IF
S l
ine
64)]
-[(U
S b
ank
pri
me
loan
rat
e (I
FS
lin
e 60
P)-
Ch
ang
es i
n U
S co
nsu
mer
pri
ce (
IFS
lin
e 64
X)]
Do
mes
tic
real
in
tere
st r
ate
dif
fere
nti
al
fro
m U
S r
ate
12 m
ch
ang
e [L
end
ing
rat
e (I
FS
lin
e 60
P)-
Do
mes
tic
CP
I(IF
S l
ine
64)]
-[(U
S b
ank
pri
me
loan
rat
e (I
FS
lin
e 60
P)-
Ch
ang
es i
n U
S co
nsu
mer
pri
ce (
IFS
lin
e 64
X)]
Fo
reig
n l
iab
ilit
ies
to f
ore
ign
ass
ets
T
he
rati
o o
f F
ore
ign
lia
bil
itie
s o
f th
e b
ank
ing
sec
tor
(IF
S l
ine
26C
) to
Fo
reig
n a
sset
s o
f th
e b
ank
ing
sec
tor
(IF
S l
ine
26C
)
Fo
reig
n l
iab
ilit
ies
to f
ore
ign
ass
ets
12 m
ch
ang
e T
he
rati
o o
f F
ore
ign
lia
bil
itie
s o
f th
e b
ank
ing
sec
tor
(IF
S l
ine
26C
) to
Fo
reig
n a
sset
s o
f th
e b
ank
ing
sec
tor
(IF
S l
ine
26C
)
Fo
reig
n r
eser
ves
in
mo
nth
s o
f im
po
rts
R
atio
of
Fo
reig
n r
eser
ves
min
us
go
ld (
IFS
lin
e 1L
.D)
to I
mp
ort
(IF
S l
ine
71)
Fo
reig
n r
eser
ves
in
mo
nth
s o
f im
po
rts
12 m
ch
ang
e R
atio
of
Fo
reig
n r
eser
ves
min
us
go
ld (
IFS
lin
e 1L
.D)
to I
mp
ort
(IF
S l
ine
71)
Fo
reig
n r
eser
ves
12
m %
ch
ang
e IF
S l
ine
1L.D
M2
to r
eser
ves
Rat
io o
f N
arro
w m
on
ey (
IFS
lin
e 34
)+Q
uas
i m
on
ey (
IFS
lin
e 35
) to
Fo
reig
n r
eser
ves
min
us
go
ld (
IFS
lin
e 1L
.D)*
Mar
ket
exc
han
ge
rate
(IF
S
lin
e A
E)/
1000
M2
to r
eser
ves
12
m c
han
ge
Rat
io o
f N
arro
w m
on
ey (
IFS
lin
e 34
)+Q
uas
i m
on
ey (
IFS
lin
e 35
) to
Fo
reig
n r
eser
ves
min
us
go
ld (
IFS
lin
e 1L
.D)*
Mar
ket
exc
han
ge
rate
(IF
S
lin
e A
E)/
1000
Sh
ort
-ter
m c
apit
al f
low
s to
GD
P
R
atio
of
Sh
ort
-ter
m c
apit
al f
low
s (B
IS T
able
8)/
1000
to
GD
P a
t cu
rren
t p
rice
s (I
FS
lin
e 99
B)/
Mar
ket
exc
han
ge
rate
(IF
S l
ine
RF
)
Sh
ort
-ter
m c
apit
al f
low
s to
GD
P
12 m
ch
ang
e R
atio
of
Sh
ort
-ter
m c
apit
al f
low
s (B
IS T
able
8)/
1000
to
GD
P a
t cu
rren
t p
rice
s (I
FS
lin
e 99
B)/
Mar
ket
exc
han
ge
rate
(IF
S l
ine
RF
)
Sh
ort
-ter
m e
xte
rnal
deb
t to
res
erv
es
R
atio
of
Sh
ort
-ter
m e
xter
nal
deb
t (B
IS T
able
9A
B)
to F
ore
ign
res
erv
es m
inu
s g
old
(IF
S l
ine
1L.D
)
Sh
ort
-ter
m e
xte
rnal
deb
t to
res
erv
es
12 m
ch
ang
e R
atio
of
Sh
ort
-ter
m e
xter
nal
deb
t (B
IS T
able
9A
B)
to F
ore
ign
res
erv
es m
inu
s g
old
(IF
S l
ine
1L.D
)
83
TA
BL
E A
4.1
The
Lis
t of L
eadi
ng In
dica
tors
(Con
tinu
ed)
Type
Lea
din
g I
nd
icat
ors
T
ran
sfo
rmat
ion
So
urc
es
Current Account C
urr
ent
acco
un
t b
alan
ce t
o G
DP
Rat
io o
f C
urr
ent
acco
un
t b
alan
ce (
IFS
lin
e 78
AL
)*M
ark
et e
xch
ang
e ra
te (
IFS
lin
e A
F)
to G
DP
at
curr
ent
pri
ces/
1000
Cu
rren
t ac
cou
nt
bal
ance
to
GD
P
12 m
ch
ang
e R
atio
of
Cu
rren
t ac
cou
nt
bal
ance
(IF
S l
ine
78A
L)*
Mar
ket
ex
chan
ge
rate
(IF
S l
ine
AF
) to
GD
P a
t cu
rren
t p
rice
s/10
00
Ex
po
rts
12 m
% c
han
ge
IFS
lin
e 70
Imp
ort
s 12
m %
ch
ang
e IF
S l
ine
71
Rea
l ef
fect
ive
exch
ang
e ra
te,
dev
iati
on
fro
m
tren
d-H
P F
ilte
r B
loo
mb
erg
(JP
Mo
rgan
RE
ER
)
Rea
l ex
chan
ge
rate
ag
ain
st U
S$
dev
iati
on
fro
m
tren
d-H
P f
ilte
r D
om
esti
c C
PI
(IF
S l
ine
64)/
Mar
ket
exc
han
ge
rate
(IF
S li
ne
AE
)*U
S w
ho
lesa
le p
rice
in
dex
(IF
S l
ine
63)*
100/
aver
age(
Do
mes
tic
CP
I(IF
S
lin
e 64
)/M
ark
et e
xch
ang
e ra
te (
IFS
lin
e A
E)
*US
wh
ole
sale
pri
ce i
nd
ex (
IFS
lin
e 63
)
Tra
de
bal
ance
to
GD
P
R
atio
of
Ex
po
rt (
IFS
lin
e 70
)-Im
po
rt (
IFS
lin
e 71
)*M
ark
et e
xch
ang
e ra
te (
IFS
lin
e A
E)
to G
DP
at
curr
ent
pri
ces
(IF
S l
ine
99B
)*10
00
Tra
de
bal
ance
to
GD
P
12 m
ch
ang
e R
atio
of
Ex
po
rt (
IFS
lin
e 70
)-Im
po
rt (
IFS
lin
e 71
)*M
ark
et e
xch
ang
e ra
te (
IFS
lin
e A
E)
to G
DP
at
curr
ent
pri
ces
(IF
S l
ine
99B
)*10
00
Financial Sector D
epo
sits
to
M2
R
atio
of
Dem
and
dep
osi
ts (
IFS
lin
e 24
)+T
ime,
sav
ing
& f
ore
ign
cu
rren
cy d
epo
sits
(IF
S l
ine
25)
to N
arro
w m
on
ey (
IFS
lin
e 34
)+Q
uas
i m
on
ey (
IFS
lin
e 35
)
Dep
osi
ts t
o M
2 12
m c
han
ge
Rat
io o
f D
eman
d d
epo
sits
(IF
S l
ine
24)+
Tim
e, s
avin
g &
fo
reig
n c
urr
ency
dep
osi
ts (
IFS
lin
e 25
) to
Nar
row
mo
ney
(IF
S l
ine
34)+
Qu
asi
mo
ney
(IF
S l
ine
35)
Do
mes
tic
cred
it t
o G
DP
Rat
io o
f D
om
esti
c cr
edit
(IF
S l
ine
32)
to G
DP
at
curr
ent
pri
ces
(IF
S li
ne
99B
)
Do
mes
tic
cred
it t
o G
DP
12
m c
han
ge
Rat
io o
f D
om
esti
c cr
edit
(IF
S l
ine
32)
to G
DP
at
curr
ent
pri
ces
(IF
S li
ne
99B
)
Do
mes
tic
real
in
tere
st r
ate
L
end
ing
rat
e (I
FS
lin
e 60
P)-
Do
mes
tic
CP
I (I
FS
lin
e 64
)
Do
mes
tic
real
in
tere
st r
ate
12 m
ch
ang
e L
end
ing
rat
e (I
FS
lin
e 60
P)-
Do
mes
tic
CP
I (I
FS
lin
e 64
)
Len
din
g-d
epo
sit
rate
sp
read
Len
din
g r
ate
(IF
S l
ine
60P
)-D
epo
sit
rate
(IF
S l
ine
60L
)
Len
din
g-d
epo
sit
rate
sp
read
12
m c
han
ge
Len
din
g r
ate
(IF
S l
ine
60P
)-D
epo
sit
rate
(IF
S l
ine
60L
)
Lo
ans
to d
epo
sits
Rat
io o
f L
oan
s, a
sset
s in
ban
k's
bal
ance
sh
eets
(IF
S 22
A t
o 2
2G)
to D
eman
d d
epo
sits
(IF
S li
ne
24)+
Tim
e, s
avin
gs
& f
ore
ign
cu
rren
cy
dep
osi
ts (
IFS
lin
e 25
)
Lo
ans
to d
epo
sits
12
m c
han
ge
Rat
io o
f L
oan
s, a
sset
s in
ban
k's
bal
ance
sh
eets
(IF
S 22
A t
o 2
2G)
to D
eman
d d
epo
sits
(IF
S li
ne
24)+
Tim
e, s
avin
gs
& f
ore
ign
cu
rren
cy
dep
osi
ts (
IFS
lin
e 25
)
M1
to G
DP
Rat
io o
f N
arro
w m
on
ey (
IFS
lin
e 34
) to
GD
P a
t cu
rren
t p
rice
s (I
FS
99B
)
M1
to G
DP
12
m c
han
ge
Rat
io o
f N
arro
w m
on
ey (
IFS
lin
e 34
) to
GD
P a
t cu
rren
t p
rice
s (I
FS
99B
)
M2
mu
ltip
lier
Rat
io o
f N
arro
w m
on
ey (
IFS
lin
e 34
)+Q
uas
i m
on
ey (
IFS
lin
e 35
)/R
eser
ve
mo
ney
(IF
S l
ine
14)
M2
mu
ltip
lier
12
m %
ch
ang
e R
atio
of
Nar
row
mo
ney
(IF
S l
ine
34)+
Qu
asi
mo
ney
(IF
S li
ne
35)/
Res
erv
e m
on
ey (
IFS
lin
e 14
)
Rea
l co
mm
erci
al b
ank
dep
osi
ts
12 m
% c
han
ge
Dem
and
dep
osi
t (I
FS
lin
e 24
)+T
ime,
sav
ing
s &
fo
reig
n c
urr
ency
dep
osi
ts (
IFS
lin
e 25
)/D
om
esti
c C
PI
(IF
S l
ine
64)
84
TA
BL
E A
4.1
The
Lis
t of L
eadi
ng In
dica
tors
(Con
tinu
ed)
Type
Lea
din
g I
nd
icat
ors
Tra
nsf
orm
atio
n
So
urc
es
Fiscal Account C
entr
al b
ank
cre
dit
to
th
e p
ub
lic
sect
or
to G
DP
Rat
io o
f C
entr
al b
ank
th
e p
ub
lic
sect
or
(IF
S 1
2A, 1
2B, 1
2C &
12B
X)
to G
DP
at
curr
ent
pri
ces
(IF
S li
ne
99B
)
Cen
tral
ban
k c
red
it t
o t
he
pu
bli
c se
cto
r to
GD
P
12 m
ch
ang
e R
atio
of
Cen
tral
ban
k t
he
pu
bli
c se
cto
r (I
FS
12A
, 12B
, 12C
& 1
2BX
) to
GD
P a
t cu
rren
t p
rice
s (I
FS
lin
e 99
B)
Fis
cal
bal
ance
to
GD
P
R
atio
of
Go
ver
nm
ent
fisc
al b
alan
ce (
IFS
lin
e 80
) to
GD
P a
t cu
rren
t p
rice
s (I
FS
lin
e 99
B)
Fis
cal
bal
ance
to
GD
P
12 m
ch
ang
e R
atio
of
Go
ver
nm
ent
fisc
al b
alan
ce (
IFS
lin
e 80
) to
GD
P a
t cu
rren
t p
rice
s (I
FS
lin
e 99
B)
Go
ver
nm
ent
con
sum
pti
on
to
GD
P
R
atio
of
Go
ver
nm
ent
con
sum
pti
on
(IF
S l
ine
91F
) to
GD
P a
t cu
rren
t p
rice
s (I
FS
lin
e 99
B)
Go
ver
nm
ent
con
sum
pti
on
to
GD
P
12 m
ch
ang
e R
atio
of
Go
ver
nm
ent
con
sum
pti
on
(IF
S l
ine
91F
) to
GD
P a
t cu
rren
t p
rice
s (I
FS
lin
e 99
B)
Net
cre
dit
to
go
ver
nm
ent
to G
DP
Rat
io o
f N
et c
laim
s o
n g
ov
ern
men
t (I
FS
lin
e 32
AN
) to
GD
P a
t cu
rren
t p
rice
s (I
FS
lin
e 99
B)
Net
cre
dit
to
go
ver
nm
ent
to G
DP
12
m c
han
ge
Rat
io o
f N
et c
laim
s o
n g
ov
ern
men
t (I
FS
lin
e 32
AN
) to
GD
P a
t cu
rren
t p
rice
s (I
FS
lin
e 99
B)
Global Economy O
il p
rice
IFS
lin
e 76
AA
Oil
pri
ce
12 m
% c
han
ge
IFS
lin
e 76
AA
Rea
l U
S$/
yen
ex
chan
ge
rate
d
evia
tio
n f
rom
tr
end
-HP
fil
ter
100*
Jap
an C
PI
(IF
S li
ne
64)/
(Jap
an m
ark
et e
xch
ang
e ra
te (
IFS
lin
e R
F)*
US
wh
osa
les
pri
ces
ind
ex (
IFS
lin
e 63
))/
aver
age(
Jap
an C
PI
(IF
S
lin
e 64
)/Ja
pan
mar
ket
ex
chan
ge
rate
(IF
S l
ine
RF
)*U
S w
ho
sale
s p
rice
s in
dex
(IF
S li
ne
63)
US
an
nu
al g
row
th r
ate
U
S B
ure
au o
f E
con
om
ic A
nal
ysi
s
US
rea
l in
tere
st r
ate
U
S b
ank
pri
me
loan
rat
e (I
FS
lin
e 60
P)-
Ch
ang
es i
n U
sco
nsu
mer
pri
ces
(IF
S l
ine
64X
)
US
rea
l in
tere
st r
ate
12 m
ch
ang
e U
S b
ank
pri
me
loan
rat
e (I
FS
lin
e 60
P)-
Ch
ang
es i
n U
sco
nsu
mer
pri
ces
(IF
S l
ine
64X
)
Real Sector D
om
esti
c co
nsu
mer
pri
ce i
nd
ex
12 m
% c
han
ge
IFS
lin
e 64
Ind
ust
rial
/m
anu
fact
uri
ng
pro
du
ctio
n
ind
ex
12 m
% c
han
ge
CE
IC /
In
do
nes
ian
Cen
tral
Bo
ard
of
Sta
tist
ics
Sto
ck p
rice
in
dex
in
lo
cal
curr
ency
12
m %
ch
ang
e B
loo
mb
erg
85
TABLE A4.2 The List of Leading Indicators for the Capital Account
Rank Leading Indicators A B C D Good Bad NSR CPC
1 Short-term capital flows to GDP 10 1 66 222 0.1316 0.0045 0.0341 0.9091
2 Short-term capital flows to GDP3 3 1 73 210 0.0395 0.0047 0.1201 0.7500
3 Foreign reserves in months of imports 2 2 74 222 0.0263 0.0089 0.3393 0.5000
4 Foreign reserves2 12 12 64 211 0.1579 0.0538 0.3408 0.5000
5 Foreign reserves in months of imports3 20 20 56 192 0.2632 0.0943 0.3585 0.5000
6 Domestic real interest rate differential from US rate3 3 29 1 73 0.7500 0.2843 0.3791 0.0938
7 Deposits in BIS banks to reserves3 4 24 0 30 1.0000 0.4444 0.4444 0.1429
8 M2 to reserves3 10 17 66 206 0.1316 0.0762 0.5794 0.3704
9 M2 to reserves 1 2 75 233 0.0132 0.0085 0.6468 0.3333
10 Domestic real interest rate differential from US rate 3 24 8 83 0.2727 0.2243 0.8224 0.1111
11 Short-term external debt to reserves3 40 117 36 106 0.5263 0.5247 0.9969 0.2548
12 Foreign liabilities to foreign assets3 34 130 42 93 0.4474 0.5830 1.3031 0.2073
13 Short-term external debt to reserves 4 21 72 214 0.0526 0.0894 1.6979 0.1600
14 Foreign liabilities to foreign assets 4 49 72 186 0.0526 0.2085 3.9617 0.0755
15 Deposits in BIS banks to reserves 0 6 4 60 0.0000 0.0909 #DIV/0! 0.0000
TABLE A4.3 The List of Leading Indicators for the Current Account
Rank Leading Indicators A B C D Good Bad NSR CPC
1 Current account balance to GDP 11 1 41 126 0.2115 0.0079 0.0372 0.9167
2 Real effective exchange rate1 45 33 31 202 0.5921 0.1404 0.2372 0.5769
3 Exports2 22 19 54 204 0.2895 0.0852 0.2943 0.5366
4 Trade balance to GDP3 5 5 71 218 0.0658 0.0224 0.3408 0.5000
5 Current account balance to GDP3 7 8 36 116 0.1628 0.0645 0.3963 0.4667
6 Real exchange rate against US$1 23 37 53 198 0.3026 0.1574 0.5203 0.3833
7 Trade balance to GDP 27 83 49 152 0.3553 0.3532 0.9942 0.2455
8 Imports2 10 54 66 169 0.1316 0.2422 1.8404 0.1563
TABLE A4.4 The List of Leading Indicators for the Financial Sector
Rank Leading Indicators A B C D Good Bad NSR CPC
1 Loans to deposits3 12 8 64 215 0.1579 0.0359 0.2272 0.6000
2 M1 to GDP3 9 6 67 217 0.1184 0.0269 0.2272 0.6000
3 Domestic real interest rate3 4 27 0 75 1.0000 0.2647 0.2647 0.1290
4 M1 to GDP 5 5 71 230 0.0658 0.0213 0.3234 0.5000
5 Lending-deposit rate spread 6 23 5 84 0.5455 0.2150 0.3941 0.2069
6 Deposits to M23 6 7 70 216 0.0789 0.0314 0.3976 0.4615
7 Domestic real interest rate 4 22 7 85 0.3636 0.2056 0.5654 0.1538
8 M2 multiplier2 50 91 26 132 0.6579 0.4081 0.6203 0.3546
9 M2 multiplier 37 71 39 164 0.4868 0.3021 0.6206 0.3426
10 Domestic credit to GDP3 30 64 46 159 0.3947 0.2870 0.7271 0.3191
11 Real commercial bank deposits2 58 161 18 62 0.7632 0.7220 0.9460 0.2648
12 Domestic credit to GDP 44 184 32 51 0.5789 0.7830 1.3524 0.1930
13 Loans to deposits 4 76 72 159 0.0526 0.3234 6.1447 0.0500
14 Deposits to M2 0 3 76 232 0.0000 0.0128 NA 0.0000
15 Lending-deposit rate spread3 0 43 4 59 0.0000 0.4216 NA 0.0000
86
TABLE A4.5 The List of Leading Indicators for the Fiscal Account
Rank Leading Indicators A B C D Good Bad NSR CPC
1 Government consumption to GDP 65 67 11 168 0.8553 0.2851 0.3334 0.4924
2 Government consumption to GDP3 48 53 28 170 0.6316 0.2377 0.3763 0.4752
3 Net credit to government to GDP3 14 14 38 113 0.2692 0.1102 0.4094 0.5000
4 Fiscal balance to GDP3 32 40 44 183 0.4211 0.1794 0.4260 0.4444
5 Fiscal balance to GDP 21 39 55 196 0.2763 0.1660 0.6006 0.3500
6 Central bank credit to the public sector to GDP 24 55 52 180 0.3158 0.2340 0.7411 0.3038
7 Net credit to government to GDP 26 114 26 25 0.5000 0.8201 1.6403 0.1857
8 Central bank credit to the public sector to GDP3 10 49 66 174 0.1316 0.2197 1.6700 0.1695
TABLE A4.6 The List of Leading Indicators for the Global Economy
Rank Leading Indicators A B C D Good Bad NSR CPC
1 Real US$/yen exchange rate1 11 1 65 234 0.1447 0.0043 0.0294 0.9167
2 US annual growth rate 11 2 65 233 0.1447 0.0085 0.0588 0.8462
3 US real interest rate3 4 1 72 222 0.0526 0.0045 0.0852 0.8000
4 US real interest rate 17 7 59 228 0.2237 0.0298 0.1332 0.7083
5 Oil price 63 121 13 114 0.8289 0.5149 0.6211 0.3424
6 Oil price2 0 6 76 217 0.0000 0.0269 NA 0.0000
TABLE A4.7 The List of Leading Indicators for the Real Sector
Rank Leading Indicators A B C D Good Bad NSR CPC
1 Stock price index in local currency2 7 55 21 58 0.2500 0.4867 1.9469 0.1129
2 Industrial/manufacturing production index2 0 0 4 79 0.0000 0.0000 NA NA
3 Domestic consumer price index2 0 27 76 196 0.0000 0.1211 NA 0.0000
87
CHAPTER 5
PREDICTING INDONESIA CURRENCY CRISES
USING THE DISCRETE CHOICE MODEL
5.1. Introduction
This chapter is the second chapter dedicated to development of an early
warning system (EWS) model to predict Indonesia’s currency crisis episodes.
Towards this goal, this chapter will employ the discrete choice probit and logit
models to predict these crisis episodes. Unlike the signal approach that was
applied in previous chapter, the logit/probit models simultaneously evaluate
the overall effects of the explanatory variables (Komulainen and Lukkarila,
2003). In addition, information that includes the probability of a crisis
occurring, and marginal contribution of each indicator, can easily be
summarized. It is also possible to apply a standard statistical test to assess the
model (Komulainen and Lukkarila, 2003). This study differs from previous
studies that have applied the probit/logit model in explaining and predicting
the currency crises and which have mostly involved multiple country analysis
by focusing on a single country. In addition, to capture the recent crisis
episodes, this study extends the sample period up to September 2008.
The discussion is organized as follows. Section 5.2 describes the previous
literature on the application of a probit/logit EWS model. Section 5.3 discusses
the basic concepts of the proposed EWS model. Section 5.4 presents the
empirical findings of the application of probit model in predicting the
Indonesian currency crises. Finally, the conclusion of this chapter will be
presented in Section 5.5.
88
5.2. Literature Review
This section explores and discusses the previous studies that applied the
discrete choice logit and probit models. Studies in this field can be divided into
two groups. Some studies attempt to explain the phenomena of currency crises
and to highlight the common factors related to these events. Other studies focus
on applying the discrete choice model as an EWS model to predict future
currency crises.
In examining the common determinant factors of the crises, most studies have
used multi-country or cross-country analyses in two ways. The first approach
points out that the currency turmoil was mainly caused by the deteriorating
domestic economic and financial circumstances. The second approach argues
that in the 1997 Asian Financial Crisis, a sudden shift in market expectations
and confidence led to regional and global contagion.
Examining the situation, Frankel and Rose (1996) applied a probit model to
analyze currency crashes for 105 developing countries from 1971 to 1992. They
found that low foreign direct investment (FDI), low reserves, high domestic
credit growth, high northern interest rates, and overvaluation of the real
exchange rate tended to trigger the occurrence of a currency crash.
Furthermore, they noted that current account and government budget deficits
could not explain the currency crises. Similarly, Goldfjan and Valdés (1997a),
who analyzed the situation three-months ahead of the binary crisis using a
panel of data from 26 countries, from May 1984 to May 1997, found that the real
exchange rate overvaluation had predictive power in explaining crises, but that
market expectations failed when it came to predicting currency crises.
Falcetti and Tudela (2006) argued that a currency crisis is a dynamic event with
each past crisis having an effect on later crisis events. Employing a dynamic
probit model by adding a lagged dependent variable among their regressors in
92 developing and emerging markets from 1970 to 1997, they found that
macroeconomic variables, financial debt and global variables, as well as the
previous banking crisis could determine the presence of the currency crisis. The
89
underlying factors of each currency crisis differed by types of exchange rate
regime, and moreover, countries that sharply devalued in the past were less
prone to experience a currency crisis in the future.
Komulainen and Lukkarila (2003) indicated that the currency crisis could be
explained by looking at indicators that included US interest rates, indebtedness
variables, such as private sector liabilities, public debt, and foreign liabilities of
banks, as well as traditional variables, such as unemployment and inflation, and
banking crises. Edwards (1989) also noted that the probability of devaluation
increased with the appreciation of the real exchange rate and the deterioration
of foreign asset position of the central bank. Isolating variables that are the
important predictors of a crisis, Berg and Pattillo (1999b) found that the
probability of a currency crisis increased when the bilateral real exchange rate
was overvalued relative to its trend, or when there was low reserve growth, low
export growth, and high growth of M2/reserves. In addition, they pointed out
that the presence of a large current account deficit and high ratio of M2 to
reserves increased the risk factor. In another study, Berg and Pattillo (1999a)
pointed out that high credit growth, an overvalued real exchange rate relative
to its trend, and a high ratio of M2 to reserves, as well as a large current account
deficit, increased the probability of a crisis.
Unlike other studies, Lau and Isabel (2005) argued that the standard probit
model cannot capture the role of a central bank in defending their domestic
currency against speculation because the prediction is only between occurrence
(1) and non-occurrence (0) in relation to a speculative attack on the currency. By
using a multi-state nested logit model, they could measure the probabilities of
speculative attacks and the probabilities of successful defenses by using nine
quarterly explanatory variables for 16 countries. They found that the
probability of speculative attacks increased whenever there was a high ratio of
short-term external liabilities to foreign reserves, a large fiscal deficit, and
appreciation of the real exchange rate.
In contrast, in the case of Korea, Park and Rhee (1998) argued that
macroeconomic indicators failed to predict the crisis because the pre-crisis
90
trends for most explanatory variables were still positive. They highlighted that
financial liberalization tends to expose the possibility of speculative attacks on
domestic financial markets. Furthermore, their study shows that a currency
crisis can hit any country and that even their domestic financial markets are
partially and indirectly open to foreign investors. Komulainen and Lukkarila
(2003) found that the capital account liberalization in the intermediate exchange
rate regimes increased the occurrence probability of the currency crises.
Examining the timing of a crisis, they found that the currency crises tended to
occur two-years after the liberalization of domestic financial sectors, or 4.5 years
after the liberalization of capital flows. Moreover, in determining the common
factors of the speculative attack on currencies, their model considers a set of
domestic political and economic variables. Eichengreen et al. (1996), also find a
contagion1 variable as the determining factor, which is statistically significant.
In this part of the study the application of the discrete choice probit/logit as an
EWS model to predict the currency crisis will be discussed. In relation to the
previous empirical work about crisis models, potential explanatory variables
are provided that may be useful for constructing and improving the
performance of an early warning system model. Even though Goldfjan and
Valdes (1997a) argued that “exchange rate crises are largely unpredictable
events”, some researchers prove that the logit/probit early warning system
model is a useful tool to predict crises. As examples, Esquivel and Larraín
(1998) were able to predict correctly most of the currency crises that occurred
within their sample, and support an EWS as a tool to prevent such currency
crises. Jacobs et al. (2005) also showed that their EWS model could perform well
in predicting the currency crises in six Asian countries they studied. Their
model indicates that out-of-sample forecasts are significantly better than in-
sample projections.
Unlike the other researchers, Berg and Pattillo (1999a) apply and compare three
different EWS models to predict the Asian Financial Crisis: namely the panel-
1 Eichengreen et al. (1996), defined a contagion variable as the presence of either a successful or
unsuccessful speculative attack on currency elsewhere in the world.
91
based signals approach based on Kaminsky et al. (1998); the probit model based
on Frankel and Rose (1996); in addition to the cross-country regressions model
based on Sachs et al. (1996). They found that only one model, that is the signal
approach, was able to predict the crisis, but that its predictive results were not
reliable. In another work, Berg and Pattillo (1999b) compared the performance
of the signal approach and probit model approach. They showed that their
models were also able to predict the occurrence of future crises. Examining their
conclusions, which tend to be unambiguous because the signal forecasting
approach performs better than some probit models, it would appear that the
probit model is better in terms of score and goodness-of-fit, additionally; the
linear specification performs better when compared to other probit models.
In conclusion, the previous studies focusing on determining the underlying
factors of currency crises and developing sophisticated models to predict crises
are still being perfected. Nonetheless, despite the studies under review, no
specific analysis or case study has been developed for Indonesia as a single
country analysis. Hence, it is necessary to develop a specific model that is viable
for that country. The evidence of various studies in this field are somehow
mixed and not robust. However, these studies suggest that the forecasting
models may help to indicate vulnerability of crises even if their current
predictive powers are still limited.
This study sets out to predict currency crises and to discover the underlying
factors. However, in dealing with the mixed results and limited prediction
power of previous works that mostly use cross-country analysis, this study
utilizes a probit/logit EWS model and applies a single country analysis for
Indonesia. By doing so, it might be able to capture the uniqueness and the
characteristic of this country that may in turn improve the prediction power of
the models. This study will also differ from previous studies related to the way
explanatory variables are selected in that this study applies the noise-to-signal
ratio that is usually used on the non-parametric signal model. The study also
extends the sample period until 2008 to see the capabilities of this model in
capturing any other crises that may exist during this period. The
92
comprehensive explanations of the model will be presented in the following
sections.
5.3. The Discrete Choice Probit/Logit Model
Using the limited dependent variable that has value between “1” for “crisis”
and “0” for “no crisis” is not appropriate when used with the standard OLS
model. This is because there is no guarantee that �� i, the estimator of �������� can be restricted to a range between 0 to 1 (Maddala, 1983, Gujarati, 2004).
Therefore, this study follows the method of Eichengreen et al. (1995), and
Frankel and Rose (1996) which apply a probit/logit model in estimating the
binary currency crises variables. The difference between them is that while the
probit is based on the cumulative standard normal distribution that allows the
estimated values of the dependent variable in a range of [0;1], the logit uses the
cumulative logistic distribution to constrain the probability to the [0,1] interval
(Jacobs et al., 2005). Reference to this can be seen in econometric text books
such as Maddala (1983), Greene (1996), and Gujarati (2004), that provide more
detailed discussion on this model.
Empirically, it is assumed that there is an unobserved variable, or a latent
variable (�∗), which is described by the equation:
�∗ = �� � + �������������������������������������������������������������5.1� where β is the regression coefficient of the independent variable, xi is a set of
explanatory variables, and εi is the error term. The outcome of a discrete choice
model is a reflection of the regression in Equation 5.1. In other words, the
observed data (yi) is determined by the value of unobservable or latent variable
(�∗). More specifically, the positive value for �∗ will indicate the presence of a
crisis in Indonesia (yi=1), while zero or negative value for �∗ reflects no crisis events (yi=0) or
93
� = �1���������∗ > 0����0��������ℎ���� � ! ���������������������������������������������5.2� If the distribution of εi is symmetric, then
� = #10! ��with�(��)��� > −�� �� = +��� ����������with�(��)��� ≤ −�� �� = 1 − +��� ��������������������������������������5.3�
where F is the cumulative distribution function for εi. This limited dependent
variable model is then estimated, based on the maximum likelihood approach,
for which the likelihood function is:
. = /01 − +��� ��1234
/+��� ��235
�������������������������������������5.4�
= /01 − +��� ��157280+��� ��1289
�35������������������������������5.5�
where yi=(0,1) is the realization of the binary outcome.
Taking logs,
:;�. = <=�1 − ��:;01 − +��� ��1 + �:;+��� ��>9
�35������������������������5.6�
The first order condition is:
@:;�.@� = <A����� ��+��� �� + −�1 − ������ ��1 − +��� �� B �� = 0
9
�35������������������������5.7�
where f is the density, DEFG8HIJDG8HI .�This likelihood equation will be nonlinear,
requiring an iteration solution. The type of binary model is determined by the
choice of the functional form for F in Equation 5.4, connected to the
assumptions made about εi in Equation 5.1.
The Probit Model
If the error term follows the standard normal distribution, ε~N(0,1), the model
is referred to as a probit, expressed as:
94
����Prob�� = 1� = F��� �� = P QG8HI7R
���S������������������5.8� = ��� ������������������������������������
where Q��� is the standard normal density. By inserting Equation 5.8 into
Equation 5.7, the likelihood equation is:
:;�. = <V�1 − ��:;F1 − Φ��� ��J + �:;FΦ��� ��JW9
�35����������������������5.9�
taking the first order condition,
@:;�.@� = <Y��� = <Z[�Q�[��� ���[��� �� \�� = 0
9
�35
9
�35������������������������5.10�
where qi=2yi-1
5.4. The Application of the Probit/Logit EWS Model to Predicting
Indonesian Currency Crises
5.4.1. Constructing the Probit/Logit EWS Model
In predicting the currency crises in which the dependent variable is defined as a
dichotomous variable that takes the value of unity for currency crisis
occurrence and zero for no crisis, this study applies the discrete choice probit
and logit models using Equations 5.8 and 5.11. However, in the empirical
analysis the predicted probabilities calculated by both models differ only
slightly2, thus, this study only presents the analysis based on the probit model.
As in the previous chapter, when defining the currency crisis event3, this study
follows the study of Kaminsky et al. (1998) which defines the currency crisis as
2 Jacobs et. al (2005), Komulainen and Lukkarila (2003), Maddala (1993). 3 See Chapter 3 for a more detailed discussion about the currency crisis dating system.
95
an attack on the domestic currency, leading to a large depreciated domestic
currency against the US dollar, or a large depletion in the foreign reserve, or the
combination of both conditions. Furthermore, in the empirical work, the
combination of these variables can be transformed into the exchange market
pressure index (EMPI) and the currency crisis defined whenever the EMPI
crosses its threshold, three standard deviations above the index’s mean. Based
on this definition, the EMPI crossed its threshold eight times, namely
November 1978, April 1983, September 1986, August 1997, December 1997,
January 1998, May 1998 and June 1998. Following Kaminsky et al. (1998), this
study applied a 24 month crisis window or prediction horizon, so resulting in
only four currency crises episodes occurring in Indonesia from 1970 to 2008.
Three of the crises occurred in the in-sample period, and one crisis, the 1997/98
Asian Financial Crisis, happened in the out of sample period from 1996 to 2008.
Moreover in selecting the set of explanatory variables, based on the EWS and
currency crises literatures, the numbers of variables that are available and
potentially used to determine currency crises is extensive. For example, in the
previous chapter that used a non-parametric approach to predict the
Indonesian currency crises, the signal approach selected 39 of 55 explanatory
variables based on their noise-to-signal ratios being less than unity (NSR<1).
However, according to Jacob et al. (2005), and Zhuang and Dowling (2002), the
probit/logit model cannot accommodate all explanatory variables because of
too few observations and multicollinearity among its explanatory variables. In
addition, according to Kamin et al. (2007), using a large set of explanatory
variables in the probit/logit model, potentially increased the number of
explanatory variables that were statistically insignificant, in addition to
introducing a certain noise in the estimation results.
Following their argument, to limit the number of explanatory variables (unlike
Jacob et al. (2005), who used factor analysis), this study uses the noise-to-signal
ratio (NSR) from Kaminsky et al. (1998), which is commonly used in the signal
approach for evaluating and selecting variables. This method was used in the
previous chapter for evaluating and selecting the leading indicators that were
96
used to construct the composite index in the signal approach. The ranked
results are based on the lowest NSR, which utilises the most powerful indicator
in predicting crises and which was presented in Table 4.2 in Chapter 4.
Following this procedure, this study selects the top ten variables, which are
presented in Table 5.1.
9
7
TABLE 5.1 L
ist
of E
xpla
nat
ory
Var
iabl
es
No
Leading Indicators
NSR
Rank
Reference
Rationale
01
Real US$/yen exchange rate
a 0.03
01
Zhuang and Dowling (2002), Berg (1999), Ito (1999)
According to Zhuang and Dowling (2002), the depreciation of the Japanese yen
against the US dollar could pressure the regional East Asian currencies. In
addition, the fluctuation of the yen/dollar rate around PPP m
ake the business
cycle in the East Asian countries more volatile (McK
innon and Schnabl, 2003).
02
Short-term capital flows to
GDP
0.03
02
Rad
elet and Sachs (1998), Komulainen and Lukkarila
(2003)
The capital outflows are highly correlated with the currency crises in m
ost crises
events (Goldfajn and Valdes, 1997b). In m
any cases, the capital inflows increase
prior to crises and there is the presence of huge capital flight during the crises. In
addition, a sudden capital outflow m
ay cause bank failure (Calvo et al., 1993)
03
US annual growth rate
0.06
04
Kam
in et al. (2007), Zhuang and Dowling (2002)
For growth of the World economy the US economy can be associated with world
dem
and. Decline in U
S growth rate can reduce dem
and for im
ports. D
eclining
export will cause deterioration of the current account am
ong US trading partners
that increases the probability of a crisis.
04
US real interest ratec
0.09
05
Kam
in et al. (2007), Zhuang and Dowling (2002)
Increases in w
orld interest rates, especially U
S real interest rates, lead
s to an
increase in the likelihood of a currency crisis by attracting the capital outflows
from developing countries, such as Indonesia.
05
US real interest rate
0.13
07
Kam
in et al. (2007), Zhuang and Dowling (2002)
06
Loans to deposits
c 0.23
08
Zhuang and D
owling (2002), B
ussiere and Fratzscher
(2002), Jacob et al. (2005)
There is a closed link between banking crisis and currency crisis (Falcetti and
Tudela, 2006, Zhuang and Dowling, 2002). M
any variables can be used to view
the perform
ance of the banking sector; one of them
is the loan-deposit rate
spread
. Furthermore, a widening gap between lending-deposit rates can reflect
problems in the banking sector(Zhuang and Dowling, 2002).
07
M1 to GDPc
0.23
09
Bussiere and Fratzscher (2002), Tinakorn (2002)
M1 can be used to determine the liquidity in an economy. Thus, increasing M
1 will increase the liquidity, which m
ay lead to a speculative attack on domestic
currency (Eichengreen et al., 1995)
08
Real effective exchange rate
a 0.24
10
Kam
in et al. (2007), Berg (1999), C
hinn (1998), Frenkel
and G
oldstein (1989), Tornell (1998), Berg and Pattillo
(1999b), Esquivel and Larrain B (1998), Kumar et al
(2003),
The overvaluation of domestic currency reduces the country’s competitiveness
that potentially reduces their exports. This condition affects a deterioration in the
current account that leads to a currency crisis in m
any countries, as well as the
banking crisis through corporate distress that increases the non-perform
ing loan
(Zhuang and Dowling, 2002).
09
Exportsb
0.29
12
Berg and Pattillo (1999b), Komulainen and Lukkarila
(2003), Kumar et al (2003), Zhuang and Dowling (2002),
Bussiere and Fratzscher (2002),
Tinakorn (2002),
Kam
insky et al. (1998), Kam
in et al. (2007), Berg et al.
(2005), Jacob et al. (2005)
10
M1 to GDP
0.32
13
Bussiere and Fratzscher (2002), Tinakorn (2002), Berg et
al. (2005), Jacob et al. (2005)
Note: a deviation from trend-H
P filter, b 12 months percentage change, c 12 months change
98
Based on this table, all variables except for three, start from 1971, namely: the
current account balance to GDP; the 12 month change of short-term capital
flows to GDP; and the 12 month change of the domestic real interest rate.
Unlike the signal approach, and according to Mironova (2007), the probit/logit
model requires consistent data intervals, so variables measured at different
points in time will be eliminated and replaced by other variables which are
ranked below. Table 5.1 lists ten explanatory variables that will be used by the
probit/logit models.
After the discussion of explanatory variables considered in this study, the
descriptive statistics of these variables are presented in following Table 5.2.
TABLE 5.2 The Descriptive Statistics
SYMBOL MAX MIN MEAN MEDIAN STDEV # OF SAMPLE
kx8 0,03 0,00 0,01 0,01 0,01 300
cy1 518,00 -47,15 22,42 12,44 51,13 300
cy4 72,26 -55,86 1,86 -0,09 27,26 300
fx3 0,12 0,06 0,09 0,09 0,01 300
fy3 0,02 -0,02 0,00 0,00 0,01 300
fy7 0,56 -0,73 -0,02 0,01 0,21 300
gx1 10,46 -2,72 3,86 3,85 2,92 300
gy1 12,14 -3,38 0,16 0,16 2,13 300
gy3 7,19 -1,94 3,09 3,38 2,20 300
gy4 39,58 -23,25 0,88 0,83 11,26 300 Note: Real US$/yen exchange ratea (gy4); Short-term capital flows to GDP (kx8), US annual growth rate (gy3), US real interest ratec (gy1); US real interest rate (gx1), Loans to depositsc (fy7), M1 to GDPc (fy3); Real effective exchange ratea (cy4); Exportsb (cy1); M1 to GDP (fx3); a deviation from trend-HP filter, b 12 months percentage change, c 12 months change;STDEV: standard deviation.
Following Jacob et al. (2005), this study checks whether this data set shows
correlation among two or more variables by using the correlation matrix. The
results are presented in Table 5.3. Based on the correlation matrix in Table 5.3,
this study finds that there is no multicollinearity in the data set.
99
TABLE 5.3 The Correlation Matrix
gy4 kx8 gy3 gy1 gx1 fy7 fy3 cy4 cy1 fx3
gy4 1.00
kx8 0.07 1.00
gy3 0.14 -0.04 1.00
gy1 0.09 -0.01 0.20 1.00
gx1 -0.27 -0.13 0.13 0.40 1.00
fy7 -0.09 0.07 -0.02 -0.06 0.16 1.00
fy3 -0.02 0.14 -0.18 0.05 -0.12 -0.04 1.00
cy4 -0.31 0.34 -0.06 0.07 0.09 0.04 0.13 1.00
cy1 0.13 0.12 -0.11 -0.11 -0.34 0.11 -0.12 -0.03 1.00
fx3 0.05 -0.07 -0.07 0.07 0.31 0.09 0.25 0.20 -0.28 1.00
Note: Real US$/yen exchange ratea (gy4); Short-term capital flows to GDP (kx8), US annual growth rate (gy3), US real interest ratec (gy1); US real interest rate (gx1), Loans to depositsc (fy7), M1 to GDPc (fy3); Real effective exchange ratea (cy4); Exportsb (cy1); M1 to GDP (fx3); a deviation from trend-HP filter, b 12 months percentage change, c 12 months change
5.4.2. Estimation Results
To forecast the currency crises in Indonesia, this study, using these ten
explanatory variables described above, estimates the probit model with
Huber/White robust errors and covariance, which is robust to misspecification
of the correlation within groups. In this table, the coefficients of variables
cannot be interpreted as the marginal effects of variable to the probability of a
crisis, but only to determine the direction effect of these variables on the
probability of a crisis. In addition, the numbers in parentheses are the z-
statistics as an analogue to the t-statistics in standard OLS regression that test
the null hypothesis of no significance, and an asterisk categorizes the level of
statistical significance of the explanatory variables.
Table 5.4 presents the regression results on the determinants of currency crises
in Indonesia, but the regression results are mixed and not all of them are in
accordance with what would have been expected. For example, almost all of the
variables have signs as predicted, except for the US annual growth rate and the
12 month percentage change of the US real interest rate. However, none of these
variables are statistically significant, whereas of eight variables with correct
signs, three are statistically insignificant, namely the real US$/yen exchange
100
rate when using a deviation from trend-HP filter, the US real interest rate, and
M1 to GDP.
Based on Table 5.4, the estimation results indicate that the significant predictors
for the Indonesian currency crises are (1) short-term capital flows to GDP; (2)
loans to depositsc; (3) M1 to GDPc; (4) real effective exchange ratea; and (5)
exportsb. The regression results in the following table also support Kamin et al.
(2007), who show that the use of many variables often produces a number of
variables that are not statistically significant. Furthermore, the use of monthly
data also creates imbalances in the sample because of too many tranquil periods
compared to infrequent times of crisis (Esquivel and Felipe, 1998).
TABLE 5.4 The General Model (Model 1)’s Regression Results
Variables Expected sign Regression coefficient
Constant -3.806) (-2.175)
**
Real US$/yen exchange ratea - -0.002) (-0.175)
Short-term capital flows to GDP
- -91.104) (-3.964)
*
US annual growth rate - 0.097) (1.175)
US real interest ratec + -0.061) (-1.264)
US real interest rate + 0.065) (1.231)
Loans to depositsc + 1.405) (3.238)
*
M1 to GDPc + 31.081) (1.828)
***
Real effective exchange ratea + 0.049) (4.323)
*
Exportsb - -0.017) (-2.962)
*
M1 to GDP + 28.212) (1.495)
McFadden R2 0.597) Number of observation 300.000) Note: a deviation from trend-HP filter, b 12 months percentage change, c 12 months change; * indicates statistical significance at a 1% level, ** indicates significance at a 5% level, and *** indicates significance at a 10% level
101
When determining the contributory factors related to currency crises in
emerging markets, Kamin et al. (2007) initially applied the larger or broad
regression model. They then dropped the least significant explanatory variable
from their “broad model” until all explanatory variables were statistically
significant, leading to their “boiled down model”. In other words, only the
significant variables were retained in the model. This study also found several
insignificant variables in the initial regression model. To make it simple,
sequentially the least significant variables were removed from the “general
model” or “model 1” until all variables were significant. In this study, this new
model is called the “specific model” or “model 2”.
Based on the regression results in Table 5.4, there are five variables that are not
significant, the real US$/yen exchange ratea being the least significant. Once
this variable is dropped, the model is re-estimated. The results indicate that
there are still four insignificant variables, with the US annual growth rate being
the least significant. After this is dropped, the model is regressed again and two
insignificant variables found. Of these two, M1 to GDPc is the least significant
variable. After deleted this variable, the model is re-estimated and only one
insignificant variable is found, namely the US real interest rate. However, after
this is dropped, the model still shows one insignificant variable, namely the US
real interest ratec. Finally after deleting this variable, the model is regressed
again and as expected it is found that all the remaining five variables are
significant and have signs as expected. The regression results are presented in
Table 5.5.
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TABLE 5.5 The Specific Model (Model 2)’s Regression Results
Variables Expected sign Regression coefficient
Constant -2.895) (-2.300)
**
Short-term capital flows to GDP - -81.811) (-3.753)
*
Loans to depositsc + 1.440) (3.141)
*
Real effective exchange ratea + 0.052) (6.113)
*
Exportsb - -0.019) (-3.358)
*
M1 to GDP + 24.566) (1.797)
***
McFadden R2 0.597) Number of observation 300.000) Note: a deviation from trend-HP filter, b 12 months percentage change, c 12 months change; * indicates statistical significance at a 1% level, ** indicates significance at a 5% level, and *** indicates significant at a 10% level
To determine the relative contribution of the remaining five variables on the
probability of currency crises in Indonesia, this study also calculates the
marginal effect for all variables, as presented in Table 5.6. This table shows that
the most influential factors for the occurrence of currency crises in Indonesia are
the short-term capital flows to GDP, followed by M1 to GDP, loans to deposits,
real effective exchange rate, and exports.
TABLE 5.6 Determinants of Indonesian Currency Crises
No Leading Indicators Expected Sign dprob/dx
01 Short-term capital flows to GDP - -9.600 02 M1 to GDPc + 2.883 03 Loans to depositsc + 0.167 04 Real effective exchange ratea + 0.006 05 Exportsb - -0.002
Note: Note: a deviation from trend-HP filter, b 12 months percentage change, c 12 months change; dprob/dx coefficient represents the marginal effect of the change in explanatory variable to the change of the probability of a crisis.
5.4.3. Predicting Indonesian Currency Crises
In this section, the study tests the predictive power of these two models by
simulating the probability of a currency crisis, utilising Figures 5.1 and 5.2. The
predictions will be divided into two periods: the in-sample period ranging from
January 1971 to December 1995, which is presented in Figure 5.1, and the out-
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of-sample period ranging from January 1996 to September 2008, which is
presented in Figure 5.2. The “general model” or model 1 is represented by a
black solid line (or probit_M1), and the “specific model” or model 2 is
represented by the colour red (or probit_M2). In addition, the yellow shaded
areas (or cc_24m) show the 24-month pre-currency crisis episodes. The
performance of these models is reported in Table 5.6 in the next section.
In-sample Prediction
As indicated in Figure 5.1, both models move in the same pattern and perform
well in capturing all three in-sample currency crises, namely November 1978,
April 1983 and September 1986. In predicting the crisis in November 1978, the
model sends warning signals for Indonesia, with the probability being about 76
percent for model 1 and 82% for model 2 in August 1976. This is then reduced
to 63% before shifting to around 80 percent in December 1976. The probability
remains high until January 1978, or 10 months before the crisis occurred, as the
probability of a crisis declined to around 40% for model 1 and 37% for model 2.
Then their probabilities tend to fluctuate with the average being about 50%
until the crisis date in November 1978.
For the next crisis, both models start sending warning signals from April 1983
with the probability about 50% that a crisis would happen in Indonesia within
24 months. Afterwards, the probability increases gradually and remains high at
around 98%. In the last in-sample currency crisis, both models are also able to
predict this crisis by sending warning signals from November 1983 or 36
months before the crisis date with the probability of Indonesia being hit by a
crisis being about 18% for model 1 and 34% for model 2. These probabilities
then increase gradually and reach 97% for model 1 and 95% for model 2. After
that their probabilities decrease gradually to below 10% in September 1986.
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FIGURE 5.1 The Probit Models: In-Sample Prediction
However, both models also send false signals because these signals are not
followed by any currency crises within 24 months. For example, from January
1993, or one month after the Bank of Indonesia closed Bank Summa
International, one of the ten largest private banks in Indonesia, the probability
of a crisis for both models increased gradually from 14% to 82% for model 1 and
75% for model 2 in December 1993, before falling significantly a month later.
Out-of-Sample Prediction
Testing the ability of this model to predict a currency crisis, this study attempts
to predict the out-of-sample currency crises from January 1996 to September
2008. According to the model crisis definition based on the Equation 3.2 in
Chapter 3, the Asian Financial Crisis in 1997/98 was the only one out-of-sample
currency crisis during this period. This is shown in the model of probability of a
crisis displayed in Figure 5.2. In this figure, the probability of a crisis for both
models also moves in the same pattern along the out-of-sample periods.
In predicting the Asian Financial Crisis in 1997/98, both models start sending
warning signals for the crisis in January 1997 with the probability of a crisis
about 66% for model 1 and 50% for model 2. In June 1997, this increases
gradually to 80% for model 1 and 70% for model 2. After that their probability
0%
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tends to decline to around 10% in October 1997 before they increase to reach to
100% in January 1998, where they remain until the crisis date in June 1998.
FIGURE 5.2 The Probit Models: Out-of-Sample Prediction
Although by the model’s crisis definition, there was no longer any currency
crisis occurring after the Asian Financial Crisis of 1997/98 to 2008, both models
still sent out warning signals, as their probability of a crisis tended to increase
during this period. As the purpose of this study is to develop EWS models to
predict currency crises in Indonesia, any signal transmitted by the models but
not followed by any currency crises within 24 months are categorized as false
signals. From Figure 5.2, it is also clear that both models have sent more false
signals in the last decade.
For example the probability of a crisis in both models remained high until
December 1999. In addition, even though not too significant, the probability of a
crisis in the two models rose from July to September 2000. Furthermore, the
probability of a crisis in both models also increased significantly to 100% from
April to June 2001, but had dropped in July 2001, only to rise again in August to
peak above 90% in December 2001. It remained at this high level until March
2002. Similarly, in July 2003, the probability of a crisis for both models also
fluctuated, for after a rise to 80% for 3 months, it then fell, but rose again to
0%
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around 50% from April to June 2004. Furthermore, the probabilities of the two
models also tended to rise towards 2008.
Interestingly those false signals occurred before, or coincided with, serious
domestic political events. The link between political activity and the crises
might explain why the probability of a crisis in both models tended to rise
during these periods. Furthermore, some scholars have found there are strong
links between political events or activities leading to financial crises. For
example, the 1997/98 currency crisis in Indonesia transformed into a multi-
dimensional crisis which affected the banking and financial sector, social and
political spheres, and culminated in a national leadership crisis that finally
ended 32 years of the “New Order” regime in 1998 (Djiwandono, 2001). After
President Soeharto stepped down in May 1998, he was replaced by Vice
President BJ Habibie as the third president of Republic of Indonesia who
remained in that position until the election of a new president in the general
election of 1999. The above figure also shows that the probability of a crisis in
both models during the transition from the “new order” regime to the
“reformation” regime was also high. These results are in accord with the results
of the study of Ghosh and Ghosh (2003), which indicate that during the period
of transition government the country became more vulnerable to
macroeconomic shocks and corporate sector weaknesses.
The link between the crisis and political activity appears to have also applied to
Indonesia. For example, the in-sample currency crisis events in 1978, 1983 and
1986 occurred just before or after general election in 1977, 1982 and 1987.
Furthermore, the Asian Financial Crisis in 1997 happened to coincide with the
general election of 1997. This is consistent with studies conducted by Mei and
Guo (2004) which found the relationship between the transition period and
general election to the financial crisis coincided with eight of nine of the
financial crises that occurred. Mei and Guo (2004) also found that general
elections were a major factor of the financial crisis because of increases in
market volatility and political uncertainty. Similarly, Vaaler et al. (2005) argue
that general elections in developing countries increase the investment risk
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premium that substantially influences the price and availability of capital in
these countries. Therefore, the increase in risk premium will boost capital
outflows. In addition, Table 5.6 indicates that short-term capital flows relative
to GDP is a dominant factor to determine the probability of a crisis in Indonesia,
with the increase of this variable by 1% increasing the probability of a crisis by
almost 10%. Similar results were also obtained by Ito (1999) who studied the
movement of capital in Asia. He argued that unlike Thailand and Korea, the
movement of capital flows in Indonesia cannot be explained without
considering the political and social shocks.
Another factor that might have caused these two models to send more false
alarms during this period, particularly in 2008, was the existence of the sub-
prime mortgage crisis in USA that sparked the Global Financial Crisis that hit
many countries around the world. Unlike the 1997 Asian Financial Crisis, the
impact of this crisis in Indonesia has been relatively weak (Basri and Rahardja,
2010). However, this crisis might affect the perception of investors, which in
turn might encourage capital flight (Basri and Rahardja, 2010, Titiheruw et al.,
2009), which raises the probability of a crisis for both models. This was shown
in a significant stock index fall from 2830 on 9 January 2008 to 1155 on 20
November 2008, by a credit default swap (CDS) that rose sharply from 250 in
early 2008 to 980 bps in November 2008, and also by an increase in the yield of
government bonds from 10 to 20%. The impact of the latter can be appreciated
when noted that an increase of 1% caused an additional cost to government
debt of Indonesian rupiah 1 trillion (Ministry of Finance of Republic of
Indonesia, 2010).
5.4.4. The Probit EWS Model’s Performance Evaluation
In this subsection, the study evaluates the performance of both models in
predicting the currency crises in Indonesia, either within sample or out-of-
sample. Furthermore, this study employs the same evaluation methods used in
Chapter 4. Thus the results of this evaluation can be used to compare the
performance of all models developed in this study, for example, the signal
approach in Chapter 4 and the artificial neural network model in Chapter 6, and
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later in Chapter 7. For this evaluation, this study set four cut-off probabilities
(or, Pr*), namely 20%, 30%, 40% and 50%, so that a crisis will happen when the
probability of a crisis of these two models exceeds these thresholds, otherwise
there will be no crisis.
In terms of the ability to predict the whole observation, either for crisis or
tranquil periods, both models show positive performance, as they are able to
predict more than 88% (model 1) and 87% (model 2) at Pr*=20% and show a
further increase to 89% (model 1) and 90% (model 2) when Pr* = 50%. But for
the out-of-sample period especially from 1996 to 1998, both models show poor
performance because they are only able to predict around 40%, and when Pr*
was increased to 50%, the ability of both models decreased to above 30%.
During this period, model 1 was slightly better than model 2. However, when
the evaluation period was extended to 2008, model 2 was slightly better than
model 1, with Pr* being higher than 40%.
As for the ability to predict the time of crisis, both models predict the in-sample
currency crises quite well, for their prediction reaches around 94% with
Pr*=20%, although their prediction power drops following a further increase in
Pr*. When Pr*=50%, they are only able to predict 75% (model 1) and 76% (model
2). In contrast, their ability to predict the out-of-sample currency crises reached
53% at Pr*=20%, and as Pr* increased to 50%, their prediction power decreased
to 43% (model 1) and 40% (model 2). On the other hand, model 2 was slightly
better than model 1 in predicting the tranquil periods for both within and out-
of-sample but both models failed to capture any tranquil period between 1996
and 1998.
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TABLE 5.7 The Probit Model’s Performance Evaluation
Note: Pr*: Cut-off probability, M1 refers to the general model or model 1, M2 refers to the specific model or model 2
Regarding the number of false alarms, these results confirm that these two
models perform much better in predicting the in-sample currency crises
compared to the out-of-sample period. For example the number of in-sample
false alarms was around 30% at Pr*=20%, decreasing to 20% at Pr*=50%.
However, the out-of-sample false alarms reached around 80% at Pr*=20% and
dropped slightly to 77% at Pr*=50%. Regarding the number of false signals sent
by both models during the in-sample period, model 1 sent more false alarms
than model 2, and vice versa in the out-of-sample period, where model 2 sent
more false alarms compared to model 1. These results are in accordance with
that shown in Figure 5.2 where the number of false signals from both models
increased in the last decade.
Pr* Assessment Methods
In-sample Out-of-sample
1971-1995 1996-1998 1996-2008
M1 M2 M1 M2 M1 M2
20%
% of observations correctly called 88.67% 87.33% 44.44% 44.44% 48.37% 46.41%
% of crisis periods correctly called 94.44% 94.44% 53.33% 53.33% 53.33% 53.33%
% of tranquil periods correctly called 86.84% 85.09% 0.00% 0.00% 47.15% 44.72%
% of false alarms of total alarms 30.61% 33.33% 27.27% 27.27% 80.25% 80.95%
QPS 0.2267 0.2533 1.1111 1.1111 1.0327 1.0719
GSB 0.0150 0.0200 0.0988 0.0988 0.2222 0.2491
30%
% of observations correctly called 90.00% 90.00% 38.89% 38.89% 52.94% 51.63%
% of crisis periods correctly called 88.89% 87.50% 46.67% 46.67% 46.67% 46.67%
% of tranquil periods correctly called 90.35% 90.79% 0.00% 0.00% 54.47% 52.85%
% of false alarms of total alarms 25.58% 25.00% 30.00% 30.00% 80.00% 80.56%
QPS 0.2000 0.2000 1.2222 1.2222 0.9412 0.9673
GSB 0.0044 0.0032 0.1543 0.1543 0.1367 0.1507
40%
% of observations correctly called 91.33% 89.67% 38.89% 36.11% 56.21% 58.17%
% of crisis periods correctly called 87.50% 79.17% 46.67% 43.33% 46.67% 43.33%
% of tranquil periods correctly called 92.54% 92.98% 0.00% 0.00% 58.54% 61.79%
% of false alarms of total alarms 21.25% 21.92% 30.00% 31.58% 78.46% 78.33%
QPS 0.1733 0.2067 1.2222 1.2778 0.8758 0.1961
GSB 0.0014 0.0000 0.1543 0.1867 0.1047 0.0769
50%
% of observations correctly called 89.33% 90.00% 36.11% 33.33% 60.78% 61.44%
% of crisis periods correctly called 75.00% 76.39% 43.33% 40.00% 43.33% 40.00%
% of tranquil periods correctly called 93.86% 94.30% 0.00% 0.00% 65.04% 66.67%
% of false alarms of total alarms 20.59% 19.12% 31.58% 33.33% 76.79% 77.36%
QPS 0.2133 0.2000 1.2778 1.3333 0.7843 0.7712
GSB 0.0004 0.0004 0.1867 0.2222 0.0578 0.0452
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Furthermore, the performance of these models can also be seen by their level of
accuracy and calibration as indicated by the QPS and GSB statistics in Table 5.7.
At Pr*=20%, model 1 is more accurate than model 2 for both in-sample and out-
of-sample predictions, as well as Pr* which increased up to 40%. However,
when Pr*=50%, model 2 is slightly more accurate than model 1, except for the
out-of-sample from 1996 to 1998.
Given that the purpose of this study is to build a model that has the ability to
predict the currency crises in the future, when deciding which model is the best,
this study focuses on the ability of the model in predicting the crisis period
compared to the tranquil period. In addition, the out-of sample prediction
results are preferred to the in-sample predictions. Recall that based on the
model’s currency crisis definition, the Asian Financial Crisis in 1997/98 was the
only crisis that occurred in the out-of-sample period 1996 to 2008. The extension
of the evaluation period from 1996 to 2008 is only to show the ability of both
models to capture the tranquil periods rather than times of crisis, and to
calculate the number of false alarms sent by these models during the period. So,
based on all the assessment methods utilised during the crisis period (1996-
1998), this study indicates that model 1 is better than model 2, particularly at
Pr*=40% or more.
5.5. Conclusions
This chapter develops the discrete choice probit/logit model, one of the most
popular EWS models to predict currency crises in Indonesia. It applies the
method of evaluation and selection of indicators of the non-parametric model of
the signal approach from Kaminsky et al. (1998). Based on this method, this
study selects 10 of the best variables that cover the same span of time starting
from 1971. Following the study of Kamin et al. (2007), this study initially used
these ten variables for the general model or model 1. It then reduced the
investigation to the specific model or model 2 by dropping the least significant
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explanatory variables from the initial model, until all explanatory variables
were statistically significant.
Based on the regression results, this study finds strong evidence that the short-
term capital flows to GDP, M1 to GDP and loans to deposits ratios, real effective
exchange rates and exports are reliable predictors of currency crises in
Indonesia. Compared with other variables, the short-term capital flows to GDP
ratio is a major contributor in determining the probability of a crisis.
Based on the prediction simulation for both in-sample and out-of-sample
periods, as shown in Figures 5.1 and 5.2, and the performance evaluation in
Table 5.7, both models can predict all the currency crises both within and out-
of-sample periods. However, related to the ability of predicting the out-of-
sample currency crises and the performance evaluation during the Asian
Financial Crisis from 1996 to 1998 in Table 5.7, the general model or model 1
performed better than the specific model or model 2, especially at Pr*=40% or
more. These findings also support that currency crises can be predicted. These
models can be used as EWS models to prevent the occurrence of currency crises
in the future.
Although these models are able to predict the occurrence of currency crises in
Indonesia, it should be noted that in the last decade, these models have sent out
a number of false alarms, particularly during the transition of government and
related political events, especially the general election. This indicates that these
models have difficulty in distinguishing currency crises from political
instabilities.
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CHAPTER 6
PREDICTING INDONESIAN CURRENCY CRISES
USING THE ARTIFICIAL NEURAL NETWORK MODEL
6.1. Introduction
To search for an appropriate early warning system (EWS) model to predict
currency crises in Indonesia, Chapters 4 and 5 explored two standard EWS
models, that is, the signal model and probit/logit model. Previous studies
indicated that these models have been successful in identifying some problems
associated with economic vulnerability, but their results are mixed and not
robust, particularly in predicting currency crises in Indonesia. Nevertheless,
being dominated by linear assumptions, these models are still criticized for
their weaknesses in ascertaining the exact timing of any given crises (Chinn,
2006). In addition, they cannot be completely used as a substitute for the
instinctive judgment that has been widely practiced by policy-makers (Bussiere
and Fratzscher, 2002, Zhuang and Dowling, 2002).
For this reason, many economists and scholars have attempted to find
alternative models. One of the alternative methods available to predict the
currency crises is the artificial neural network (ANN) model. This model has
been used as a successful forecasting model in engineering, financial modeling
and stock market analyses, and in predicting bankruptcy. In addition,
according to Kamruzzaman et al. (2006), the application of the ANN model is
more powerful than the multiple regression models in real life problem solving,
including those found in finance and manufacturing. According to Medsker
(1996), the ANN model has several features that are not available in the
conventional methods, such as fault tolerance, generalization, and adaptability.
In addition, Nag and Mitra (1999) point out that ANN is a non-linear, data-
driven, and self-adaptive model (Fioramanti, 2008) and can be used for
113
universal function approximations. Thus, it can independently comprehend the
inherent interrelationships of the given input variables (Hill et al., 1996). It is
also able to deal with erroneous, incomplete or missing, fuzzy or noisy input
data (Kamruzzaman et al., 2006). Furthermore, the ANN model is able to
approximate any continuous function with any desired accuracy (Hornik et al.,
1989, Hornik, 1991). Moreover, the ANN model can be trained to improve its
performance significantly compared to the existing standard EWS models such
as the signal model and discrete choice model.
With these observations in mind, this study applies the ANN model to predict
currency crises in Indonesia. This differs from previous studies in that it focuses
on a single country, Indonesia, and the way that input neurons are selected in
sample periods from 1971 to 2008.
This chapter is organized as follows. Section 6.2 reviews the literature that is
related to the application of the ANN model, while the explanations of the
proposed model are presented in Section 6.3. Section 6.4 presents an application
of an ANN model for Indonesia. The conclusion will be provided in Section 6.5.
6.2. Literature Review on the Application of the ANN EWS Model
ANN models have been widely applied in many fields because of their ability
to solve complex problems. For examples ANN models are commonly applied
in the financial sector for bankruptcy prediction. However, the application of
ANN as an EWS model to predict a currency crisis is still very limited, although
some previous studies have applied an ANN model as an EWS model to
predict currency crises. For example, Nag and Mitra (1999) applied and
compared two models, namely the ANN model and signal approach for
predicting the 1997 currency crises in three Asian countries, Malaysia, Thailand
and Indonesia. For this purpose, they used data from January 1980 to December
1996 for training or developing their model and tested them using the out-of-
sample prediction from January 1996 to January 1998. They found that while
114
crises in Indonesia could be predicted, it was more difficult in the case of the
other two sample countries, Thailand and Malaysia. In addition, their signal
model failed to flag the Indonesian crises. In contrast, the ANN model was able
to capture this crisis by sending some alarm signals before the onset of the
crisis.
Peltonen (2006) used and compared two EWS models, namely the ANN model
and probit model to predict the currency crises in 24 emerging economies. The
architecture of his ANN model was composed of three layers with 17 input
neurons, and one hidden layer with two hidden neurons. For the output
neuron, he defined the currency crisis whenever the EMPI passed its threshold
which was the two standard deviations above the index mean. Furthermore he
used the Lavenberg-Marquardt (LM) learning rate instead of back-propagation
in order to accelerate the training process. His results indicated that both
models performed well in predicting the in-sample currency crises but were
found weak for the out-of-sample crises, as they were only able to predict the
Russian crisis of 1998. His models also failed to predict the Indonesian currency
crisis. In general, he found that his ANN model performed better than his
probit model.
Yu et. al (2006) used a general regression neural network1 (GRNN) model to
forecast the 1997/98 currency crisis in four Southeast Asian countries,
Indonesia, the Philippines, Singapore and Thailand. The architecture of their
model was composed of four layers with six input neurons in the input layer,
two hidden layers where the first hidden layer was the pattern layer consisting
of 8 nodes, and the second hidden layer was the summation layer. In turn, this
was divided into two parts, the numerator and the denominator, where both
parts consisted of two nodes and three output neurons in the output layer. For
the input variables, they used the currency exchange price, the rate of change of
1 GRNN is a neural network model which follows the regression model without a model
specification requirement (WALCZAK, S. & CERPA, N. 1999. Heuristic Principle for the Design
of Artificial Neural Network. Information and Software Technology, 41, 107-117.
115
price, a ten-day moving stochastic oscillator price, a ten-day moving average, a
ten-day moving variance, and a moving variance ratio. For the output neurons,
they divided the level of the 1997–1998 Southeast Asian currency crises in terms
of the currency exchange rates volatility level into three grades, green, yellow
and red. The model was trained using daily data from 2 January 1997 to 31
December 1998. For out-of-sample prediction, the model was then tested using
the data from 4 January 1999 to 31 December 2004. Their model was able to
predict the crises very accurately when compared to other forecasting methods,
such as the signal model, the logit model, the probit model, and the
discriminant analysis models2, namely linear discriminant analysis (LDA), and
quadratic discriminant analysis (QDA).
In another paper, Yu et al. (2010) applied a multi-scale neural network learning
paradigm to predict the financial crisis for Thailand and Korea. Basically, they
used the standard three layers ANN model with back propagation learning
algorithms. Unlike their previous study, to define their input neurons, they
applied the Hilbert-EMD algorithm to decompose the daily exchange rate series
into ten intrinsic mode components (IMC) for the South Korean Won and seven
IMC’s for the Thai baht. In addition, they defined the currency crisis whenever
the rate of change of the exchange rate passed its threshold of 2.5 standard
deviations above the mean. Their model was then trained using the data set
from 2 January 1996 to 31 December 1997, and tested using the out-of-sample
prediction from 2 January to 31 December 1998.
Similar results to their previous study were obtained, as their proposed model
proved very accurate when predicting crises in both South Korea and Thailand.
In addition, their ANN models were also superior to other forecasting methods,
such as the LDA model, QDA model, the signal model, the logit model, and the
probit model. Among ANN models, their BPNN (Back-Propagation Neural
2 LDA and QDA are the statistical methods for pattern recognition learning. To find out more detail on this discussion about these models, readers can refer to econometric text books such as HAIR, J. F., ANDERSON, R. E., TATHAM, R. L. & BLACK, W. C. 1995. Multivariate Data Analysis, New York, Prentice-Hall.
116
Network) performed better than their GRNN model. These findings indicate
that the ANN model provides a promising model for predicting currency crises.
Franck and Schmied (2003) also applied a multilayer ANN with two hidden
layers to predict the twin crises in Russia and Brazil in 1998/99. They found
that the Russian and Brazilian crises were quite similar to the Asian financial
crisis that hit Thailand, Malaysia, the Philippines and Indonesia, which were
partly caused by the self-fulfilling expectation of foreign investors. Even though
their model was able to predict the onset of the Russian and Brazilian crises,
their results were not overwhelmingly convincing. However, they still believe
that the ANN model is a promising tool of prediction when it comes to
forecasting the onset of a crisis. Similarly, when Fioramanti (2009), applied the
ANN model to predict sovereign debt crises in 46 developing countries from
1980 to 2004, he found that his ANN model outperformed the pooled and the
conditional (or fixed effect) logit model.
These previous studies demonstrate that ANN models can be used as a
promising tool to forecast currency crises or EWS models. This study also
applies an ANN model as an alternative EWS model to predict currency crises
in Indonesia. The approach differs from the previous study in terms of the data
sets used for training, particularly when applying the noise-to-signal ratio from
Kaminsky et al. (1998), which is commonly used in the signal approach for
evaluating and selecting input variables. In addition, this study uses the set of
variables that are statistically significant in the probit model. Apart from
predicting the 1997 crisis, this study also extends the period analysis from 1971
up to 2008 to test whether the developed models are able to identify other crises
that happened during the period 1999 to 2008. A more detailed explanation of
the ANN model will be provided in the following section.
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6.3. Specification of the ANN Model
The application of neural network analysis increased substantially in the 1980s
due to the rapid development in computer technology and progress in the
innovation of new features, such as techniques and algorithms in neural
network analysis itself. However, among ANN models for business analysis,
there are two very successful applications of this model, such as with the
multilayered feed-forward neural network, and the self-organizing map (Smith,
2002). The first represented a neural network with supervised training
(Rumelhart et al., 1986) that can be used for prediction and classification. In
contrast, the self-organizing map represents a neural network with
unsupervised learning (Kohonen, 1982, Kohonen, 1988) that can be used for
clustering.
Basically, the artificial neural network mimics the operation of a biological
neural network. The biological neural system consists of a simple structure that
performs three basic functions: receiving signals (input) from other neurons;
processing these signals; and then sending them to other neurons. Using the
same analogues with this biological neural system, the ANN model mimics the
structure of the biological neurons by connecting all neurons from the input to
output layer.
6.3.1. Architecture of the ANN Model
The architecture of the ANN model can be divided into three layers, as shown
in Figure 6.1. The first layer is known as the input layer, the last layer as the
output layer, and any intermediate layers between the input and output layer
are hidden layers. In addition, each layer in a neural network model consists of
a number of neurons or nodes, and one bias neuron is added, its value always
being 1 for both input and hidden layers. This is because, according to Kecman
(2001), in the logistic and sigmoid activation function, if there is no bias neuron
in a hidden layer, the learning algorithm (that is, back-propagation) cannot
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learn because it can control the shapes, orientation and steepness of all types of
data mapping.
Like a human brain, all neurons in these layers are fully connected to other
neurons in the next layers but there are no connections between neurons within
a layer and each connection is associated with a weight. Basically, in the feed-
forward neural network, all neurons including the bias neuron will send all
signals to other neurons in the next layer in a forward direction.
FIGURE 6.1 Architecture of the ANN Model
Input Layer
In developing the neural network, the first step is to select the appropriate
indicators for input variables to ensure the precision of the prediction of output
- even the type or the numbers of neurons in the input layer are determined by
researchers in relation to their research objectives. However, the selection of
potential independent variables is important (Walczak and Cerpa, 1999, Zhang
et al., 1998). This is because, for a supervised learning neural network, the more
relevant explanatory variables with the output variable will reduce the model’s
learning times. A more detailed discussion about the selection of input neurons
will be presented in the section on empirical findings.
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Hidden Layer
In Figure 6.1, the next layer or any intermediate layers between input and
output layers are hidden layers. Several issues arise in the hidden layer, such as
the appropriate number of hidden layers and the number of neurons for each
layer. Regarding the number of the hidden layers, in a neural network model,
using more than one hidden layer is also possible. In addition, if there is more
than one hidden layer in the network, the signals from the first hidden layer
will be distributed to the next hidden layer before reaching the output layer,
and this will increase learning time for the model to converge.
Another issue is related to the appropriate number of neurons in a hidden
layer. There is a trade-off between putting too many neurons or too few
neurons in this layer. Using too many neurons will effect a longer learning
period for the model, sometimes leading to the model being over fitted with
data that causes the model to perform poorly when adding new data, because it
starts to model the noise in the data set (Svozil et al., 1997, Walczak and Cerpa,
1999). On the other hand, if the numbers of neurons in the hidden layer is too
small, the neural network will have a problem dealing with a complex data set
(Zhang et al., 1998, Walczak and Cerpa, 1999).
Output Layer
The last layer in Figure 6.1 is the output layer. The output neuron in the output
layer corresponds to the predicted variable. In this study, the expected output is
only one, the probability of a currency crisis in Indonesia having a value in the
range of 0 to 1. Additional information about the output neuron will be
explained in the empirical section. In this model, to guarantee the output
neuron falls into this range, the logistic activation function will be utilized. To
train this model in the supervised learning neural network, this output neuron
will be compared with the target.
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6.3.2. The Learning Algorithm
As mentioned earlier, like a human brain, the ANN model can be trained to
make its prediction more accurate and powerful. There are many supervised
learning algorithms which are available but the most popular and commonly
used is the back-propagation (Werbous, 1974, Wong et al., 2000). More than 95
percent of the application of neural networks in business apply this learning
algorithm (Wong et al., 2000). As already mentioned, all neurons from the input
to output layer are connected, and each connection has a weight associated with
it. In this supervised learning, as with back-propagation, the objective of the
training process is to discover the appropriate weight for every connection
among neurons in all layers.
According to Fausett (1994), to train a neural network using the back
propagation method can be done in three steps, as follows:
Step 1 – Feed-Forward
In this step, all signals travel from the input to the output layer. Using the feed-
forward mechanisms, all the signals from all neurons in the input layer (Xi)
together with one bias neuron are sent to the neurons in the hidden layer, their
initial weights (vij) being set to small random values. In the hidden neuron, two
processes apply, as seen in Figure 6.2.
FIGURE 6.2 A Single Hidden Neuron
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After receiving the signals from input neurons, in every hidden neuron, all
these incoming signals are summed using the following equation:
�� = ��� +����
(6.1) In this study, the preference output of the neural network model is the
probability of a crisis, which is valued in the range of 0 for “no crisis” to 1 for
“crisis”. For this purpose, this study applies a logistic sigmoid activation
function to keep its value in this range. In order to make it consistent with the
preference output, in this hidden layer, after the summation of all incoming
signals, this value needs to be transformed to any value in the range of 0 and 1
using the same activation function (logistic sigmoid) before sending it to the
next layer, using the following equation:
�� = ����� = 11 + exp�−���(6.2)
For simplicity, this study only uses one hidden layer, so these activation signals
(Zj) are sent to the next layer, the output layer. In this output neuron (Yk), a
similar process with a hidden neuron is also applied. First, all incoming signals
from hidden neurons are summed up using the following equation:
�� = ��� +�������
(6.3)
Then, as the expected value of the output neuron is the probability of a crisis in
the range of 0 to 1, so this summation value will be transformed to any value in
the range of 0 to 1 using a logistic sigmoid activation function:
�� = �(��) = 11 + �� (−��)(6.4)Step 2 - Back Propagation of Error
In this step, the network’s output (Yk) is compared with the target (Ok) and all
the errors from this comparison are back propagated from the output to the
input layer via a hidden layer, as follows:
122
" = 0.5�%&� − ��'(�
(6.5)In the back-propagation mechanism, as the error is a function of weights, then
the network will minimize this error by adjusting the connection weight of each
neuron in the entire layer. By use of the chain rule,
)")��� =))��� *0.5�%&� − ��'(
�+ = −%&� − ��'�,(��)��(6.6)
Where: f’ is the derivative of the activation function.
To make it more convenient, this study defines δk as the portion of the error
correction weight adjustment for wjk, or
-� = %&� − ��'�,(��)(6.7) Using Equation 6.7, Equation 6.6 can be rewritten as follows:
)")��� = −-��� (6.8) For weights connecting the hidden unit (Zj) to the input unit (Xi):
)")�� = −�%&� − ��' ))�����(6.9)
= −�%&� − ��'�′(��)�
))�� �� Using Equation 6.7, Equation 6.9 can be rewrite as follows:
)")�� = −�-��
))�� ��(6.10)
= −�-���� ))�� ��
= −�-���� �′�����
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To make it more convenient, this study defines δj as the portion of the error
correction weight adjustment for vij,, or
-� =�-���� �,�����
(6.11) Using Equation 6.11, Equation 6.10 can be rewritten as follows:
)")�� = −-��(6.12) Step 3 – Adjusting the Associated Weights
Finally, the associated weight involved in each connection among neurons in all
layers is gradually adjusted. As already mentioned, in the back-propagation
mechanism, the error will be adjusted gradually in a backward direction. So,
first, the model updates the weights on connections between output neurons to
hidden neurons. After that, this model updates the connection weight between
hidden to input neurons. Basically, the new connection weight (���∗ ) between
output and hidden neuron can be updated through the summation of the old
connection weight (wjk) with the weight correction term (∆wjk), or
���∗ = ��� + ∆���(6.13) In addition, the back-propagation algorithm is the optimizing technique using
the steepest descent algorithm that requires a step size or learning rate (α) to be
specified (Zhang and Berardi, 2001), so the weight correction term (∆wjk) can be
obtained using the following equation:
∆��� = −4 )")��� (6.14) In this case, the error as function of weight means that the error will be
minimized by adjusting the connection weight (Fausett, 1994) and the minus
sign (-) in a direction in which the function decreases more rapidly.
Using Equation 6.8, this (Equation 6.14) can be rewritten as:
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∆��� = 4-��� (6.15) Gradient descent in a back-propagation algorithm which faces the problem of
slow convergence (Svozil et al., 1997, Tang et al., 2006, Peltonen, 2006). To deal
with this problem, Tang et. al (2006) introduced the momentum factor (β),
which is a positive constant between 0 and 1 in the above equation. Using
momentum, it can increase the convergence speed and also avoid a local
minimum because it can smooth the connection weight (Torii and Hagan, 2002),
and when combined with the learning rate parameter, it can also improve the
performance of the gradient descent significantly (Bishop, 1995, Lek et al., 1996).
Thus, the correction term to update the weight for hidden unit (Zj) is
∆���(5) = 4-��� + 6∆���(5 − 1)(6.16) where t denotes the iteration number.
The correction term to update the weight of a biased unit in the hidden layer
will be:
∆�7�(5) = 4-� + 6∆�7�(5 − 1)(6.17) After updating the weight from the output neuron to the hidden neuron, the
next step is to update the connection weights between hidden and input units.
Similarly, new connection weights between hidden and input neurons (��∗ ) can be updated through the summation of the old weight (vij) with the weight
correction term (∆vij), or
��∗ = �� + ∆�� (6.18) Then, the proportion of the change in weight can be obtained using the
multiplication of the learning rate (α) with the derivation of weigh update, or
∆�� = −4 )")�� (6.19) Using Equation 6.12, Equation 6.19 can be rewritten as
125
∆�� = 4-��(6.20) By adding the momentum factor, the correction term to update the weight from
the hidden unit to the input unit is as follows:
∆��(5) = 4-�� + 6∆��(5 − 1)(6.21) The correction term to update the weight for the bias unit in the input layer will
be
∆�7�(5) = 4-� + 6∆�7�(5 − 1)(6.22)
This learning process will be continued until the network meets one condition
whenever the net output of the model converges to its target, or the minimum
threshold of the error is achieved. However, if the neural network never
converges to its target, setting the maximum number of iterations can stop this
learning process. So, even though this model fails to achieve its target, that is,
the minimum error, this learning process will stop whenever the maximum
number of iterations is achieved. The application of this model to predict the
Indonesian currency crises will be discussed in the next section.
6.4. The Application of the ANN EWS Model to Predicting
Indonesian Currency Crises
6.4.1. Constructing the ANN Model
In building the ANN model to predict currency crises in Indonesia, there are
several important steps. The first step is to determine the output node. Selecting
the input neurons and determining the number of hidden layers and hidden
neurons will then follow. The next step will be to determine the type of
activation function, the maximum number of iterations, the learning rate, as
well as the momentum rate.
126
Similar to previous chapters, in this section, the output neuron is the currency
crises3 in Indonesia. To determine the currency crises, this study follows the
study of Kaminsky et al. (1998) that defines the currency crisis as a speculative
attack against the local currency followed by a sharp decline in the exchange
rate, or significant decrease in foreign reserve, or the combination of both.
Empirically, a currency crisis occurs when the exchange market pressure index
(EMPI) passes its threshold that is three times its standard deviation above the
index’s mean.
The next step is to select the set of variables for the input neurons to ensure the
precision of the prediction of output. Although the ANN model can be trained
to improve its performance, according to Walczak and Cerpa (1999); Zhang et
al. (1998), the selection of input variables is very important and greatly affects
the outcome of the ANN model. However, until now, no particular methods
have been dedicated to select the input variables in this model. For example, Yu
et al. (2006) used the modification of local currency to predict the speculative
attacks on domestic currency, although in their subsequent studies, to define
their input variables, Yu et al. (2010) used the intrinsic mode components (IMC)
of two local currencies, South Korea’s Won and Thailand’s Baht, using the
decomposition method, the Hilbert-EMD algorithm.
Unlike the studies of Yu et al. (2006, 2010), this study employs the noise-to-
signal ratio for selecting a set of input variables. This is commonly used in the
signal approach to evaluate performance and to choose the set of leading
indicators. Based on this method, this study chooses the top ten variables,
which are similar to those used for the application of the probit model in
previous chapter. This model is later referred to as a “general model” or “model
1”. Table 6.1 presents the list of input variables for model 1. Thus the
performance of both models can be compared later on in Chapter 7 because the
ANN model without a hidden layer is quite similar to the discrete choice model
as seen in the probit or logit models (Tu, 1996) and also in many other studies 3 See Chapter 3 for a more detailed discussion about the currency crisis dating system.
127
that compare the performance of these models (Yu et al., 2006, Yu et al., 2010,
Tu, 1996, Dreiseitl and Ohno-Machado, 2002, Ottenbacher et al., 2004, Peltonen,
2006).
TABLE 6.1 List of Input Nodes for Model 1
No Description NSR
01 Real US$/yen exchange ratea 0.03
02 Short-term capital flows to GDP 0.03
03 US annual growth rate 0.06
04 US real interest ratec 0.09
05 US real interest rate 0.13
06 Loans to depositsc 0.23
07 M1 to GDPc 0.23
08 Real effective exchange ratea 0.24
09 Exportsb 0.29
10 M1 to GDP 0.32 Note: a deviation from trend-HP filter, b 12 months percentage change, c 12 months change.
The number of input variables used in the ANN model is also important in that
it affects the outcome of the ANN model (Walczak and Cerpa, 1999, Zhang et
al., 1998). Several studies indicate that using fewer input variables is superior
than using too many input variables because the inclusion of noise in the data
set degrades the performance of the ANN models (Walczak and Cerpa, 1999,
Yu et al., 2010). Opposite results were found by Jain and Nag (1995), the
performance of their ANN model using many input neurons being superior to
their model using fewer input neurons. Therefore Walczak and Cerpa (1999)
concluded that a reduction in the number of input neurons must be done
carefully to ensure that only variables having noise or correlated input neurons
are discarded so as to avoid performance degradation.
Taking these findings into consideration, this study contributes to the debate
because it also develops and compares two ANN models, namely the “general
model” or “model 1” and “specific model” or “model 2”, which has fewer input
neurons. As with Walczak and Cerpa (1999), in selecting the input variables for
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model 2, this study use five variables4 which are statistically significant in the
probit model applied in Chapter 5. For consistency and comparability,
following the study of Yu et al. (2010), this study, adopting model 2, uses the
same number of hidden neurons and training parameters as model 1. The list of
these input variables is presented in Table 6.2.
TABLE 6.2 List of Input Nodes for Model 2
No Description
1 Short-term capital flows to GDP
2 Loans to depositsc 3 Real effective exchange ratea 4 Exportsb 5 M1 to GDP
Note: a deviation from trend-HP filter, b 12 months percentage change, c 12 months change
Moreover, Walczak and Cerpa (1999) pointed out that the presence of
correlation among the input variables increased the noise that led to decreased
performance of the ANN models. Accordingly, based on the correlation matrix
as presented in Table 5.2, Chapter 5, this study found that there is no
multicorrelinearity among the input variables. In addition, as the dimensions of
these data sets are varied, and in order to provide equal proportional
contributions as well as to remove biases in the forecasting model, these data
are normalized before being added to the model (Hall et al., 2009). Furthermore,
according to Hamid (2004), data normalization also provides equal statistical
distribution among all input and output variables. For this purpose, and
following Hall et al. (2009), before putting these variables and input neurons in
the input layer, all data will be normalized to keep values in the range -1 to 1,
using the following equation:
�8 = 2 9 �8 −:;<(�8):=�(�8) − :;<(�8)> − 1(6.23)
4 From the general model, the least statistically significant explanatory variables were
eliminated one by one until all variables were statistically significant. For more detail, see
Chapter 5.
129
In addition, this study also adds one bias neuron, the value of which is always
one in the input layer for both models.
The next step is to determine the optimum number of hidden layers and the
number of hidden neurons for each hidden layer. In the ANN model with back-
propagation learning algorithm, according to Walczak and Cerpa (1999), it is
more flexible to determine the number of hidden layers. Even though in the
ANN model, using more than one hidden layer is also possible, many scholars
argue that one hidden layer is sufficient for the model to solve almost all
problems (Swales and Yoon, 1992, Hornik et al., 1989, Yu et al., 2007, Dreiseitl
and Ohno-Machado, 2002, Svozil et al., 1997). With regard to the number of
hidden layers, although initially using a single hidden neuron, this study also
tested this model by adding another hidden layer to see whether this additional
hidden neuron could improve the performance of the model or not. However,
similar to the study of Baum and Hassler (1989) it has been found that
additional hidden layers degrade rather than improve performance of the
model, as its error increased. Moreover, the cost of adding another hidden layer
also increases the learning time. This is because the additional hidden layer can
lead to a model trapped in a local minima, with instability of gradient descent,
so that the learning process becomes slower and needs more time (Svozil et al.,
1997).
As previously noted, one of the weaknesses of this model is a tendency to over-
fit, a tendency that increases in line with an increase in the number of hidden
neurons, which in turn lowers the ability of the ANN model (Svozil et al., 1997,
Walczak and Cerpa, 1999). However, too few hidden neurons decrease the
ability of the model to deal with a more complex data set (Zhang et al., 1998,
Walczak and Cerpa, 1999). To date, there is no agreement as to the optimal
number of hidden neurons that should be used, it being common practice to
choose an arbitrary basis in order to maximize the performance of the model or
to minimize the error (Yu et al., 2010, Zhang et al., 1998). For example, some
have claimed that the maximum number of hidden neurons should be equal to
130
the input neurons, while others claim half the number is enough, while others
recommend two-fold, or even a doubling plus 2.
While there is a wide spectrum of opinions, this study followed the advice of
Song (2010) by applying the specific-to-general method when determining the
optimal number of hidden neurons. Furthermore, the ANN model adopted
uses a variety of hidden neurons ranging from one to 22 hidden neurons, the
result of which is presented in Figure 6.3. This has allowed the optimal number
of hidden neurons to be chosen at the point where the model has the smallest
training error. Consequently, this study found that the smallest training error
was obtained when the number of hidden neurons equaled the number of input
neurons, a finding similar to the study of Chakraborty et al. (1992) and Tang
and Fishwick (1993). This figure indicates that the ANN model with the number
of hidden neurons less than the input number, that is, 10 neurons, limits the
ability of the model to deal with complex and non-linear data sets (Zhang et al.,
1998, Walczak and Cerpa, 1999). However, Svozil et al. (1997) and Walczak and
Cerpa (1999) warn that using too many hidden neurons causes the model to be
over-trained, thus potentially degrading the performance of the model because
its error increases in line with increasing the number of hidden neurons used
(see Figure 6.3). It should be noted that this study also adds one bias neuron,
the value of which is always one in the hidden layer.
As mentioned in the earlier section on methodology, this study uses the logistic
activation function in the hidden and output neurons. This is because the target
of the output neuron in these models is the probability of a crisis, which is
valued between 0 and 1. To ensure the output of these models is within this
range, this study applies the logistic activation function for the output neuron,
and for consistency, adopts the same activation function, the logistic activation
function being used in both models for every hidden neuron.
131
FIGURE 6.3 Numbers of Hidden Neurons vs. RMS Errors
In the ANN model with a back-propagation learning algorithm, there are many
factors that determine model performance. Other than those described above,
the performance of these models is determined by the number of iterations, the
value of momentum and the learning rate. Similar to Yu et al. (2010), in this
study, these factors were carried out by trial and error until the optimum
performance of these models was obtained.
The number of iterations used in this model is determined by trial and error.
Therefore in developing this ANN model, simulations were carried out with
different numbers of iterations to achieve the smallest training error, such as
10,000 (0.0971), 20,000 (0.0812), 30,000 (0.0622), 40,000 (0.0622) and 50,000
(0.0628). These simulation results indicate that although the iteration of 30,000
and 40,000 has the same error rate, higher iteration rates require more time for
convergence or training. Based on this argument, this study found that the most
efficient iteration number was 30,000.
Likewise, trial and error was utilized to determine the optimal learning rate and
momentum rate. According to Tang et al. (1991), a high learning rate is more
suitable for a less complex model, however, the combination of low learning
0,174
0,101
0,085
0,081
0,071
0,0630,066
0,064 0,0640,062
0,076
0,0710,075
0,0670,065
0,069 0,068
0,077
0,0720,068
0,0700,066 0,066
0,05
0,07
0,09
0,11
0,13
0,15
0,17
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 19 20 21 22
RM
S e
rro
rs
Numbers of hidden neurons
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rate and high momentum rate is more suitable for a complex model. Based on
their argument, this study sets the learning rate at 0.01 and the momentum rate
at 0.8 and found the training error to be equal to 0.0622. This study also tested
the model by raising the learning rate to 0.1 and 0.3 but found the training
errors increased to 0.1486 and 0.4365, respectively. Likewise, when the lower
momentum rate was adopted, the training error increased to 0.0765 and needed
a much longer training time. This simulation confirms the finding of Tang et al.
(1991). Finally, according to Lek et al. (1996), using the optimum combination of
learning rate and momentum rate can speed up the training time and at the
same time prevent the model from becoming trapped in a local minimum.
As previously mentioned, in predicting the Indonesian currency crises, this
study applies the ANN model with the supervised learning back-propagation
algorithm. This is because back-propagation is quite a popular method
(Werbous, 1974, Wong et al., 2000), particularly for business and finance. In
addition, based on the study of Yu et al. (2010) it was found that the ANN
model with back-propagation performed better than the general regression
neural network (GRNN). In addition, poor results were achieved by Peltonen
(2006) when using the ANN model with the Lavenberg-Marquardt (LM)
algorithm instead of back-propagation.
Towards this purpose of training, this study, divides the sample period into
two sub-samples, being the in-sample period from January 1971 to December
1995, and the out-of-sample period from January 1996 to September 2008. In
order to improve the performance of both models, these ANN models will be
trained using the in-sample data, and after that both models will be tested using
the out-of-sample data set. Following this, by using the training parameters
mentioned in Table 6.3, both models will be trained. First, both models set their
initial connection weights between neurons and bias neurons in the input layer
with the hidden neuron in the hidden layers selected randomly. Using the feed-
forward, these signals always propagate toward the forward direction, from
input layer to output layer via the hidden layer.
133
Furthermore, in this hidden neuron, these models sum all these signals and
transform them in the range of 0 to 1 using the logistic activation function. In
this hidden layer, these models also randomly set the initial connection weight
between the hidden neuron and the bias neuron to output neuron. These
models then send transformed signals from the hidden to the output neuron
and a similar process also applies. All these signals are summed and using the
logistic activation function, will convert to within 0 to 1.
TABLE 6.3 Elements of Artificial Neural Network Architecture
No Training Information Model 1 Model 2
1 Type of network Multi-layer perceptron Multi-layer perceptron
2 Number of layers 3 3
3 Number of hidden layer 1 1
4 Number of input neurons 10 5
5 Number of hidden neurons 10 10
6 Number of output neurons 1 1
7 Activation functions Logistic Logistic
8 Performance function Mean squared error Mean squared error
9 Training algorithm Back-propagation Back-propagation
10 Starting weights and biases Random Random
11 Number of iterations 30000 30000
12 Training error 0.062 0.121
13 Learning rate (α) 0.010 0.010
14 Momentum factor (β) 0.800 0.800
The learning stage begins by comparing the output neuron with its target. This
error is then transferred in a backward direction from output layer to input
layer via the hidden layer using the back-propagation learning algorithm. This
learning process aims to determine the appropriate connection weights for all
neurons in both models. This study sets 30,000 as the maximum number of
iterations for both models. As a result, these models will stop the learning
process whenever these models reach the minimum error or the maximum
number of iterations. After the iterations reach 30,000, the learning error is
0.062231 (model 1) and 0.11911 (model 2). The final weights and biases for all
neurons in all layers are presented in Table A6.1 and A6.2 (model 1) and Tables
A6.3 and A6.4 (model 2).
134
In addition, based on these training processes, this study is able to indicate the
average contribution of each input neuron to the output neuron for both
models. Table 6.4 presents the contribution of input neurons to the output
neuron for model 1. From this table, the most significant contributor is the real
effective exchange rate, followed by the 12-month percentage change of loans to
deposit, and the 12-month percentage change of the US real interest rate. For
more detail, see Table 6.4.
TABLE 6.4 Average Contribution of Input Nodes to Output Node for Model 1
No Description Contribution
1 Short-term capital flows to GDP 8.64%
2 Exportsb 8.59%
3 Real effective exchange ratea 17.40%
4 M1 to GDP 6.93%
5 M1 to GDPc 7.28%
6 Loans to depositsc 13.11%
7 US real interest rate 10.55%
8 US real interest ratec 10.84%
9 US annual growth rate 6.94%
10 Real US$/yen exchange ratea 9.72% Notes: a deviation from trend-HP filter, b 12 months percentage change, c 12 months change.
Furthermore, Table 6.5 presents the contribution of input neurons to the output
neuron for model 2. Based on this table, and similar to model 1, the main
contributor to the output neuron for model 2 is also the real effective exchange
rate. This is then followed by the 12-month change of loans to deposit and the
12-month change of exports.
TABLE 6.5 Average Contribution of Input Nodes to Output Node for Model 2
No Description Contribution
1 Short-term capital flows to GDP 14.06% 2 Exportsb 18.95% 3 Real effective exchange ratea 29.22% 4 M1 to GDP 14.16% 5 Loans to depositsc 23.61%
Notes: a deviation from trend-HP filter, b 12 months percentage change, c 12 months change.
135
6.4.2. Predicting Indonesian Currency Crises
In this section, both models are used to simulate and evaluate their ability to
predict the probability of the Indonesian currency crises, as presented in Figure
6.4 for the in-sample prediction (1971-1995), and Figure 6.5 for the out-of-
sample prediction (1996-2008). In these figures, the black solid line is the
probability of a crisis for model 1 (ann_M1) and the red thin solid line is the
probability of a crisis for model 2 (ann_M2). In addition, as this study uses the
24-month of crisis window, so the yellow shaded areas are the 24 months prior
to the currency crises.
In-Sample Prediction
As earlier noted, during this period, based on the model’s currency crisis
calculation, Indonesia experienced three currency crises, namely November
1978, April 1983 and September 1986. In Figure 6.4, it is shown that both models
are able to predict these crises within 24 months prior to the crises. For example,
in the first crisis, model 1 sent warning signals with a probability of a crisis at
11% in September 1976, increasing to 36% in December 1976 (or 24 months prior
to the crisis), then jumping to 96% in the following month and to 100% in March
1977 where it remained until the crisis occurred in November 1978. Model 2
sent warning signals from August 1976 when the probability of a crisis reach
73%, reducing gradually to 17% before increasing to 48% in December 1976,
some 24 months prior to the crisis. After that, its probability of a crisis increased
to 100% and although thereafter it fluctuated, overall it remained high until the
crisis date.
For the second in-sample currency crisis, both models sent warning signals
from April 1981, with the probability of a crisis reaching 30% (model 1) and 63%
(model 2). After this the probability of a crisis for both models increased to
around 80% in the 24 months prior the crisis, and a further increase to 100% in
July 1981 (model 1) and June 1981 (model 2). With the exception of September
1981 when the probability of a crisis dropped to 83% (model 1) and 63% (model
2), their probability of a crisis
when the percentages dropped
FIGURE 6.4 The ANN Models: In
In predicting the third currency crisis,
warning signals about the occurrence of this crisis,
probability of a crisis in both models
probability of a crisis of model 1
crisis for model 2. In the 24 months p
signals with the probability of
continued to increase to 100% in January 1985. However
probability of a crisis was very high until the crisis date whe
dropped to 56%. Unlike the first model,
warning signals (thus, more tha
probability of a crisis, although fluctuating,
move positively until reaching the highest peak
gradually decreasing to 77%
during the following month.
25% in January 1986, increased
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
19
71
M0
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M0
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M0
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cc24m
136
s remain high until a month prior to the crisis date
dropped to 78% (model 1) and 59% (model 2).
The ANN Models: In-Sample Prediction
In predicting the third currency crisis, although both models were able to send
warning signals about the occurrence of this crisis, unlike the first two crises the
both models was relatively high. However,
crisis of model 1 was much higher than the probability of
. In the 24 months prior to the crisis, model 1 sent
ls with the probability of a crisis at 67%. After that, although fluctuating,
100% in January 1985. However, on ave
s very high until the crisis date when its probability
Unlike the first model, in May 1985, model 2 began to send
more than 24 months prior to the crisis)
, although fluctuating, reaching 58%. It then tended
until reaching the highest peak at 83% in February 1985,
to 77% in May 1985, before reaching its apex
the following month. Thereafter it continued to fluctuate, dropped
, increased to 55% in February 1986, and fluctuated
19
79
M0
5
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80
M0
3
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81
M0
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M1
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M0
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M0
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M0
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M0
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M0
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M1
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M0
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M0
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M0
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M0
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cc24m ann_M1 ann_M2
the crisis date
re able to send
two crises the
was relatively high. However, the
much higher than the probability of a
rior to the crisis, model 1 sent warning
although fluctuating, it
n average its
its probability
n to send
n 24 months prior to the crisis) when its
%. It then tended to
% in February 1985, then
its apex at 99%
dropped to
and fluctuated around
19
93
M0
7
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94
M0
5
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95
M0
3
137
70%, before dropping to 7% in September 1986. Since that time, the probability
of a crisis for both models has tended to fluctuate but movements have not been
significant.
Out-of-Sample Prediction
As noted in the beginning of this chapter, this study only captures one out-of-
sample currency crisis, the Asian financial crisis of 1997-98. To test the
performance of these EWS models in predicting the out-of-sample currency
crisis, this study presents the time series probability of a crisis for both models
in Figure 6.5, from January 1996 to September 2008. Both models will be
evaluated in terms of their ability to predict the Asian financial crisis and as
illustrated, both are able to predict the occurrence relatively well. Furthermore,
in general, the probabilities of a crisis in both models move in the same pattern
for the entire sample period.
In predicting this crisis, both models start sending warning signals from
January 1996 with the probability of a crisis about 66% (model 1) and 9%
(model 2). Their probability of a crisis then increases gradually to 100% in July
1996 and remains there for about six months before dropping to around 70% in
January 1997. Although in June 1997 the probability of a crisis increases to 95%
(model 1) and 99% (model 2), their probability of a crisis falls back dramatically
to 6% (model 1) and 0% (model 2) before rising again sharply to 100% in
January where it remains to June 1998 for model 1 and December 1999 for
model 2. Unlike model 2, model 1 did not send any warning signals for the next
three months with its probability of a crisis remaining low from July to
September 1998. After that time its probability of a crisis rises back to 100% in
November 1998, remains high until December 1999 and thereafter decreases.
However, unlike model 2, model 1 decreases gradually and remains low until
March 2001.
Thereafter, both models still send warning signals, as their probability of a crisis
tends to fluctuate. For example, their probability of a crisis increases to 100%
from April 2001 to March 2002 except during three months (July to September
2001) when their probability drop
send warning signals from October to December 2004 but their probability of
crisis at around 30% is not to
models again send warning signals. For example,
probability of a crisis in model
from 2008, its probability of a crisis increase
2008 where it remained until the end of sample period in September 2008. In
contrast, from the end of 2006 to 2007, model 2 sen
compared to model 1, with its
51%. Entering 2008, model 2 start
probability of a crisis around
September 2008.
FIGURE 6.5 The ANN Models: Out
Compared to the in-sample prediction presented in Figure 6.4, the out
sample prediction presented in Figure 6.5 clearly indicates that even though no
currency crisis has occurred since the Asian F
models have continued to send more signals over the last decade. Therefore,
these signals sent by both models can be categorized as false signals since these
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
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96
M0
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M0
7
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M0
1
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M0
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M0
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M0
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M0
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cc24m
138
when their probability drops significantly. Furthermore, both models also
warning signals from October to December 2004 but their probability of
not too significant. From the end of 2006 to 2008
warning signals. For example, during this period, the
model 2 fluctuated but not too significantly, however,
crisis increased significantly to reach 100% in June
until the end of sample period in September 2008. In
the end of 2006 to 2007, model 2 sent more warning signals
probability of a crisis fluctuating at an average of
%. Entering 2008, model 2 started sending warning signals in April,
crisis around 52%, and continued to increase to 100% in
The ANN Models: Out-of-Sample Prediction
sample prediction presented in Figure 6.4, the out
sample prediction presented in Figure 6.5 clearly indicates that even though no
s has occurred since the Asian Financial Crisis in 1997/98, both
send more signals over the last decade. Therefore,
these signals sent by both models can be categorized as false signals since these
20
00
M0
1
20
00
M0
7
20
01
M0
1
20
01
M0
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cc24m ann_M1 ann_M2
rthermore, both models also
warning signals from October to December 2004 but their probability of a
to 2008, both
during this period, the
, however,
100% in June
until the end of sample period in September 2008. In
more warning signals
average of
April, with the
and continued to increase to 100% in
sample prediction presented in Figure 6.4, the out-of-
sample prediction presented in Figure 6.5 clearly indicates that even though no
risis in 1997/98, both
send more signals over the last decade. Therefore,
these signals sent by both models can be categorized as false signals since these
20
07
M0
7
20
08
M0
1
20
08
M0
7
139
transmitted signals were not followed by any currency crisis within a 24-month
period.
To explain why these models send false alarms, particularly when using the
out-of-sample is quite problematic. Unlike the parametric approach for which it
is possible to identify the marginal effect of each variable relative to the
dependent variable and which makes possible identification of the main factors
causing the model to issue many false alarms, by analyzing the movement of
the main contributor variables this problem is resolved. On the other hand, one
major disadvantage of the ANN model is its “black box” nature, which leads to
this model having limitations in explaining the causal relationship between
input and output (Tu, 1996). Although, Tables 6.4 and 6.5 illustrate the
contribution of each input neuron to output neuron, outside these input nodes,
there are still many factors that affect the value of the output node, such as the
numbers of hidden layers and hidden nodes used, and the values of learning
rates and momentum, plus the number of iterations used.
6.4.3. The ANN EWS Model’s Performance Evaluation
This section has attempted to evaluate the performance of these two models
based on their ability to predict both in-sample and out-of-sample currency
crises in Indonesia. As mentioned earlier, the in-sample evaluation has been
based on the ability of these models to capture the three currency crises within
the 24 crisis windows, namely November 1978, April 1983 and September 1986.
As described before, from January 1996 to September 2008, Indonesia had only
one currency crisis, the Asian financial crisis, which occurred in 1997/98.
Therefore, this study will evaluate the performance of both models based on
their ability to predict the Asian financial crisis using the sub-sample from 1996
to 1998. In addition, this study also evaluates their performance for the entire
sample from 1996 to 2008. Evaluation methods used in this chapter are similar
to those methods used in previous chapters. Similarly, in this chapter there are
140
four cut-off-probabilities, namely 20%, 30%, 40% and 50%, and crisis occurs
when the models probability of a crisis exceeds these cut-off-probabilities or
thresholds, otherwise there is no crisis. The evaluation results for both models
can be seen in Table 6.6.
In general, the performance of the EWS model can be seen from the ability of
the model to capture the whole observation in both periods of crisis and
tranquility. As shown in Table 6.6, both models showed very good performance
in predicting the entire in-sample periods, and at Pr*=20%, both models can
capture 98% (model 1) and 94% (model 2). The ability of these models increases
in line with increasing Pr*, for example when Pr*=50%, their performance
increases to almost 100% (model 1) and 96% (model 2). Similar results were
obtained when evaluating the ability of models to predict the out-of-sample
period, both during the Asian financial crisis from 1996 to 1998 and the entire
sample from 1996 to 2008. Unlike the in-sample results where model 1 performs
slightly better than model 2, for the out-of-sample prediction, the ability of
model 1 is much better than model 2. For example, at Pr*=20%, model 1 is able
to predict 89% and 69% (entire sample), while model 2 only captures 69% and
60% (entire sample). When Pr*=50%, during the crisis period, the ability of both
models drops to 83% and 76% (entire sample) for model 1 and 56% and 70%
(entire sample) for model 2.
In detail, the performance of both models can be evaluated based on their
ability to predict the crisis periods, tranquil periods and also the number of
false alarms transmitted. With regard to the ability to predict the 24 month
period before the in-sample crises, at Pr*=20%, both models can predict very
well, being 100% (model 1) and 99% (model 2), but when Pr*=50%, the ability of
both models decreases slightly to 99% (model 1) and 93% (model 2). However,
when predicting the out-of-sample currency crisis, model 1 is more dominant
than model 2 as it can predict 97%, while model 2 is only able to predict 83% at
Pr*=20%. However, when Pr* rises to 50%, the predictive ability of both models
drops to 90% (model 1) and 67% (model 2).
141
TABLE 6.6 The ANN Model’s Performance Evaluation
Pr* Assessment Methods
In-sample Out-of-sample
1971-1995 1996-1998 1996-2008
M1 M2 M1 M2 M1 M2
20%
% of observations correctly called 98.33% 94.00% 88.89% 69.44% 69.28% 60.13%
% of crisis periods correctly called 100.00% 98.61% 96.67% 83.33% 96.67% 83.33%
% of tranquil periods correctly called 97.81% 92.54% 50.00% 0.00% 62.60% 54.47%
% of false alarms of total alarms 6.49% 19.32% 9.38% 19.35% 61.33% 69.14%
QPS 0.0333 0.1200 0.2222 0.6111 0.6144 0.7974
GSB 0.0006 0.0057 0.0062 0.0015 0.1730 0.2222
30%
% of observations correctly called 99.67% 96.00% 86.11% 63.89% 71.90% 64.05%
% of crisis periods correctly called 100.00% 97.22% 93.33% 76.67% 93.33% 76.67%
% of tranquil periods correctly called 99.56% 95.61% 50.00% 0.00% 66.67% 60.98%
% of false alarms of total alarms 1.37% 12.50% 9.68% 20.69% 59.42% 67.61%
QPS 0.0067 0.0800 0.2778 0.7222 0.5621 0.7190
GSB 0.0000 0.0014 0.0015 0.0015 0.1300 0.1436
40%
% of observations correctly called 99.67% 97.33% 86.11% 58.33% 74.51% 66.67%
% of crisis periods correctly called 98.61% 97.22% 93.33% 70.00% 93.33% 70.00%
% of tranquil periods correctly called 100.00% 97.37% 50.00% 0.00% 69.92% 65.85%
% of false alarms of total alarms 0.00% 7.89% 9.68% 22.22% 56.92% 66.67%
QPS 0.0067 0.0533 0.2778 0.8333 0.5098 0.6667
GSB 0.0000 0.0004 0.0015 0.0139 0.1047 0.0930
50%
% of observations correctly called 99.67% 96.33% 83.33% 55.56% 75.82% 69.93%
% of crisis periods correctly called 98.61% 93.06% 90.00% 66.67% 90.00% 66.67%
% of tranquil periods correctly called 100.00% 97.37% 50.00% 0.00% 72.36% 70.73%
% of false alarms of total alarms 0.00% 8.22% 10.00% 23.08% 55.74% 64.29%
QPS 0.0067 0.0733 0.3333 0.8889 0.4837 0.6013
GSB 0.0000 0.0000 0.0000 0.0247 0.0821 0.0578
In capturing the tranquil periods, model 1 consistently performs well compared
to model 2 for both periods in all levels of cut-off-probabilities. When Pr*=20%,
the difference in the ability of both models is quite large for both in-sample and
out-of-sample. However, as Pr* increases, the ability of models to capture these
tranquil period increases and their difference becomes smaller, although model
1 does perform better than model 2. This finding is also supported by the
number of false signals relative to all signals sent by both models. Based on this
ratio in Table 6.6, this study finds that model 2 sent more false signals
compared to model 1 for the entire out-of-samples from 1996 to 2008.
142
In addition, the performance of both models can also be analyzed according to
their level of accuracy and calibration. In general, both models have performed
well as indicated by the small values of QPS and GSB, in which the value of
zero indicates the perfect level of accuracy and calibration. Based on these two
measures, overall, model 1 performs better than model 2. This finding is also
consistent with other evaluation methods mentioned in the above tables that
confirm model 1 outperforms model 2 in predicting the Indonesian currency
crises for both in-sample and out-of-sample periods. Furthermore, unlike the
study of Walczak and Cerpa (1999) and Yu et al. (2010), this finding confirms
the results of Jain and Nag (1995), that the ANN model with more input
neurons is better than ANN model using fewer input neurons.
6.5. Conclusions
The application of the ANN model as an EWS model to predict the currency
crisis, particularly in the case of Indonesia, is promising as it is able to predict
almost the 24 months prior to the in-sample currency crises in Indonesia. Even
though the prediction results for the out-of-sample currency crisis are not as
good as the in-sample prediction, however, the ANN model also performs very
well in predicting the 24 months prior to the out-of-sample Indonesian currency
crisis.
Based on the assessment methods, and comparing the performance of both
models, model 1 performs much better than model 2 in its ability to predict the
in-sample and out-of-sample currency crises. The findings also support the idea
that currency crises can be predicted and that the application of the ANN
model in predicting them, particularly for Indonesia, is promising.
The real effective exchange rate and the 12-month change of loans to deposit
ratio are the main contributors for the probability of a crisis for both models.
Model 1 also indicates that the 12-month percentage change of US real interest
rate contributes significantly in determining its output. However, model 2
143
identifies that the 12-month change of exports and the short-term capital flows
to GDP ratio also contribute significantly to the determination of the probability
of a crisis.
144
Appendixes
TABLE A6.1 The Weights and Adjustment Weight from Input to Hidden Layers for Model 1
Layer Weight (v) Weight Delta (∆v)
Input Hidden Symbol Value Symbol Value
1 1 v11 3.79331 ∆v11 0.000056
2 1 v21 -1.50928 ∆v21 0.000108 3 1 v31 -1.20637 ∆v31 - 0.000276
4 1 v41 -0.31833 ∆v41 - 0.000033 5 1 v51 -3.01624 ∆v51 0.000125
6 1 v61 -8.23915 ∆v61 - 0.000118
7 1 v71 10.98769 ∆v71 0.000194
8 1 v81 -2.46586 ∆v81 - 0.000046 9 1 v91 7.37515 ∆v91 0.000053
10 1 v101 -1.72519 ∆v101 0.000120 11 1 v01 -4.29891 ∆v01 - 0.000092
1 2 v12 -0.64360 ∆v12 - 0.000029 2 2 v22 -4.35508 ∆v22 0.000140
3 2 v32 -4.60598 ∆v32 0.000015 4 2 v42 -6.08008 ∆v42 - 0.000100
5 2 v52 2.23115 ∆v52 0.000135
6 2 v62 1.15679 ∆v62 0.000142 7 2 v72 5.15782 ∆v72 - 0.000046
8 2 v82 -9.81534 ∆v82 - 0.000121 9 2 v92 2.90272 ∆v92 0.000013
10 2 v10 2 4.22672 ∆v10 2 - 0.000008
11 2 v02 0.30987 ∆v02 - 0.000065
1 3 v13 0.00664 ∆v13 0.000061
2 3 v23 3.75700 ∆v23 0.000107 3 3 v33 -10.59311 ∆v33 - 0.000081 4 3 v43 -3.06346 ∆v43 0.000004
5 3 v53 -2.75972 ∆v53 0.000082
6 3 v63 -4.09971 ∆v63 - 0.000199 7 3 v73 8.07263 ∆v73 0.000244
8 3 v83 -1.21470 ∆v83 - 0.000145
9 3 v93 6.39143 ∆v93 0.000057 10 3 v10 3 -1.35272 ∆v10 3 0.000022
11 3 v03 -0.81125 ∆v03 - 0.000080
1 4 v14 3.02614 ∆v14 0.000088 2 4 v24 0.23764 ∆v24 - 0.000013
145
TABLE A6.1 The Weights and Adjustment Weight from Input to Hidden Layers for Model 1
(continued)
Layer Weight (v) Weight Delta (∆v)
Input Hidden Symbol Value Symbol Value
3 4 v34 -0.03899 ∆v34 0.000037
4 4 v44 -1.63580 ∆v44 0.000007 5 4 v54 -1.71434 ∆v54 0.000046 6 4 v64 0.19454 ∆v64 - 0.000040
7 4 v74 0.86295 ∆v74 0.000003 8 4 v84 -0.59612 ∆v84 - 0.000025
9 4 v94 0.40911 ∆v94 - 0.000066
10 4 v10 4 -0.19716 ∆v10 4 - 0.000010 11 4 v04 1.43076 ∆v04 0.000018
1 5 v15 -1.79507 ∆v15 0.000022
2 5 v25 -0.25624 ∆v25 - 0.000092 3 5 v35 -2.51936 ∆v35 - 0.000135
4 5 v45 1.14238 ∆v45 - 0.000018
5 5 v55 1.90700 ∆v55 - 0.000082
6 5 v65 -0.03742 ∆v65 0.000029 7 5 v75 -2.34246 ∆v75 - 0.000005
8 5 v85 1.06800 ∆v85 0.000001
9 5 v95 -3.58290 ∆v95 - 0.000003 10 5 v10 5 -0.32378 ∆v10 5 - 0.000071
11 5 v05 2.00241 ∆v05 0.000100 1 6 v16 5.05771 ∆v16 0.000018
2 6 v26 4.03198 ∆v26 0.000128 3 6 v36 0.93862 ∆v36 - 0.000010
4 6 v46 2.50219 ∆v46 0.000086
5 6 v56 -6.15359 ∆v56 0.000055
6 6 v66 -6.10206 ∆v66 - 0.000001 7 6 v76 -0.71376 ∆v76 0.000066
8 6 v86 3.57601 ∆v86 - 0.000012 9 6 v96 2.86236 ∆v96 - 0.000030
10 6 v10 6 -3.67082 ∆v10 6 - 0.000048 11 6 v06 1.44063 ∆v06 - 0.000109
1 7 v17 -5.73604 ∆v17 - 0.000056 2 7 v27 6.05849 ∆v27 - 0.000070 3 7 v37 -9.32790 ∆v37 - 0.000248
4 7 v47 -2.86865 ∆v47 0.000005 5 7 v57 -0.42072 ∆v57 - 0.000030
6 7 v67 -6.69436 ∆v67 0.000086
7 7 v77 -1.47556 ∆v77 - 0.000058
146
TABLE A6.1 The Weights and Adjustment Weight from Input to Hidden Layers for Model 1
(continued)
Layer Weight (v) Weight Delta (∆v)
Input Hidden Symbol Value Symbol Value
8 7 v87 2.98994 ∆v87 0.000023
9 7 v97 1.22135 ∆v97 0.000055 10 7 v10 7 4.13062 ∆v10 7 0.000161
11 7 v07 10.05562 ∆v07 0.000069
1 8 v18 9.16944 ∆v18 0.000089 2 8 v28 -2.25900 ∆v28 0.000162
3 8 v38 2.52309 ∆v38 0.000000
4 8 v48 4.11652 ∆v48 0.000117
5 8 v58 -4.47209 ∆v58 - 0.000062
6 8 v68 -5.60264 ∆v68 - 0.000066
7 8 v78 0.87461 ∆v78 - 0.000003 8 8 v88 -0.64263 ∆v88 0.000042
9 8 v98 0.48178 ∆v98 - 0.000005 10 8 v10 8 -2.11583 ∆v10 8 - 0.000089 11 8 v08 -2.70574 ∆v08 - 0.000078
1 9 v19 -1.14149 ∆v19 - 0.000006 2 9 v29 0.34255 ∆v29 0.000104
3 9 v39 -0.84035 ∆v39 0.000105
4 9 v49 2.09797 ∆v49 0.000068 5 9 v59 -1.55615 ∆v59 - 0.000213 6 9 v69 1.53547 ∆v69 0.000004
7 9 v79 -3.22638 ∆v79 - 0.000121 8 9 v89 1.58313 ∆v89 0.000008
9 9 v99 -3.74085 ∆v99 - 0.000116 10 9 v10 9 7.59907 ∆v10 9 0.000120 11 9 v09 1.64954 ∆v09 0.000095 1 10 v110 3.98237 ∆v110 0.000086
2 10 v210 0.70985 ∆v210 0.000000 3 10 v310 -2.22015 ∆v310 - 0.000105
4 10 v410 -1.73414 ∆v410 - 0.000050 5 10 v510 -0.65714 ∆v510 - 0.000053
6 10 v610 3.46032 ∆v610 - 0.000002 7 10 v710 0.52589 ∆v710 0.000001
8 10 v810 0.66482 ∆v810 - 0.000037
9 10 v910 -0.79967 ∆v910 0.000051 10 10 v10 10 -0.84294 ∆v10 10 0.000024
11 10 v010 1.56299 ∆v010 0.000023
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TABLE A6.2 The Weights and Adjustment Weight from Hidden to Output Layers for Model 1
Layer Weight (w) Weight Delta (∆w)
Hidden Output Symbol Value Symbol Value
1 1 w11 8.22012 ∆w11 0.000173
2 1 w21 8.05748 ∆w21 - 0.000035 3 1 w31 -11.81557 ∆w31 - 0.000167
4 1 w41 -3.95643 ∆w41 - 0.000045
5 1 w51 5.81931 ∆w51 0.000052
6 1 W61 7.02754 ∆w61 - 0.000014 7 1 W71 -8.57063 ∆w71 0.000022
8 1 W81 -9.37805 ∆w81 0.000009
9 1 W91 7.71279 ∆w91 0.000144
10 1 W101 -4.73709 ∆w101 - 0.000066
11 1 w01 0.26200 ∆w01 - 0.000031
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TABLE A6.3 The Weights and Adjustment Weight from Input to Hidden Layers for Model 2
Layer Weight (v) Weight Delta (∆v)
Input Hidden Symbol Value Symbol Value
1 1 v11 -0.0334 ∆v11 0.00011
2 1 v21 11.1398 ∆v21 0.00015 3 1 v31 -5.6442 ∆v31 -0.00012
4 1 v41 -4.0072 ∆v41 -0.00019
5 1 v51 7.0809 ∆v51 0.00018
6 1 v01 -0.1922 ∆v01 -0.00004 1 2 v12 8.2227 ∆v12 -0.00015
2 2 v22 3.4059 ∆v22 0.00006
3 2 v32 -0.1190 ∆v32 0.00005
4 2 v42 -10.1258 ∆v42 -0.00007
5 2 v52 16.2961 ∆v52 0.00012
6 2 v02 1.2471 ∆v02 0.00006 1 3 v13 0.7145 ∆v13 -0.00004
2 3 v23 3.9817 ∆v23 0.00008 3 3 v33 -0.7252 ∆v33 -0.00013
4 3 v43 -8.0775 ∆v43 -0.00012
5 3 v53 3.4977 ∆v53 0.00036
6 3 v03 2.8402 ∆v03 -0.00012
1 4 v14 -1.8782 ∆v14 -0.00016 2 4 v24 5.2004 ∆v24 -0.00007
3 4 v34 -3.2810 ∆v34 -0.00001 4 4 v44 -0.3858 ∆v44 0.00006
5 4 v54 5.0386 ∆v54 0.00027 6 4 v04 1.1258 ∆v04 -0.00004
1 5 v15 -24.6426 ∆v15 -0.00045
2 5 v25 7.6951 ∆v25 0.00004 3 5 v35 -15.0260 ∆v35 -0.00028 4 5 v45 -7.1960 ∆v45 -0.00009
5 5 v55 12.4219 ∆v55 0.00033
6 5 v05 15.7090 ∆v05 0.00020 1 6 v16 0.6352 ∆v16 0.00000
2 6 v26 1.9198 ∆v26 0.00002 3 6 v36 -1.0198 ∆v36 -0.00002
4 6 v46 0.4504 ∆v46 0.00001
5 6 v56 0.2992 ∆v56 0.00002
6 6 v06 0.7232 ∆v06 0.00002
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TABLE A6.3 The Weights and Adjustment Weight from Input to Hidden Layers for
Model 2 (continued)
Layer Weight (v) Weight Delta (∆v)
Input Hidden Symbol Value Symbol Value
1 7 v17 -0.7268 ∆v17 0.00001
2 7 v27 4.0865 ∆v27 0.00023
3 7 v37 -2.5184 ∆v37 -0.00003 4 7 v47 0.5646 ∆v47 -0.00001
5 7 v57 0.0617 ∆v57 0.00001
6 7 v07 1.0739 ∆v07 -0.00004 1 8 v18 -1.0847 ∆v18 -0.00015
2 8 v28 0.9248 ∆v28 -0.00015 3 8 v38 -2.5657 ∆v38 -0.00016 4 8 v48 0.9038 ∆v48 -0.00002
5 8 v58 0.5833 ∆v58 0.00001
6 8 v08 -0.0928 ∆v08 -0.00005 1 9 v19 0.4316 ∆v19 0.00000
2 9 v29 1.3301 ∆v29 0.00000 3 9 v39 -0.3654 ∆v39 0.00000 4 9 v49 0.2382 ∆v49 0.00000
5 9 v59 0.0814 ∆v59 0.00001 6 9 v09 0.0871 ∆v09 0.00001
1 10 v110 8.2650 ∆v110 0.00026
2 10 v210 -10.8962 ∆v210 0.00020 3 10 v310 7.6095 ∆v310 0.00005 4 10 v410 6.1864 ∆v410 -0.00004
5 10 v510 2.5224 ∆v510 -0.00034 6 10 v010 -10.7778 ∆v010 0.00004
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TABLE A6.4 The Weights and Adjustment Weight from Hidden to Output Layers for Model 2
Layer Weight (w) Weight Delta (∆w)
Hidden Output Symbol Value Symbol Value
1 1 w11 -7.9749 ∆w11 -0.00011
2 1 w21 -11.5476 ∆w21 0.00002 3 1 w31 5.9794 ∆w31 0.00002
4 1 w41 -4.2249 ∆w41 -0.00013
5 1 w51 7.1779 ∆w51 0.00010
6 1 W61 1.1031 ∆w61 0.00004 7 1 W71 4.0469 ∆w71 0.00015
8 1 W81 2.6501 ∆w81 0.00011
9 1 W91 0.1909 ∆w91 0.00000
10 1 W101 9.3942 ∆w101 0.00016
11 1 w01 3.2862 ∆w01 0.00004
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CHAPTER 7
EARLY WARNING SYSTEM MODELS:
COMPARISON AND CONSISTENCY
7.1. Introduction
In the preceding chapters three models have been developed in an effort to find
a suitable early warning system (EWS) model for predicting Indonesian
currency crises, namely the signal model in Chapter 4; discrete choice, that is,
probit and logit models in Chapter 5; and the artificial neural network (ANN)
model in Chapter 6. In those chapters, the performance of each model was
evaluated using six assessment methods.1 Based on these assessment methods,
the models generally performed well in predicting the crises. However, because
a comparison across the models has not been attempted, this study has not
determined which is the best of these three EWS models for predicting currency
crises in Indonesia.
This chapter will, therefore, attempt to achieve three objectives: first, to evaluate
and compare the performance of these three EWS models in order to define the
best EWS model in predicting Indonesian currency crises using the 24-month
crisis window for both in-sample and out-of-sample; second, to analyse the
sensitivity and consistency of these models when the underlying assumption of
crisis window or prediction horizons is shortened from 24 months to 6, 12 and
18 months; third, to compare the prediction results of these models for these
shorter crisis windows. This approach will allow an assessment of the
consistency and sensitivity across the three models and will thus allow for a
comparison to ascertain if the results obtained using the benchmark crisis
window are still valid at these shorter crisis windows. In addition, it also adds
1 Please see Chapter 4 for a more detailed explanation of the assessment methods used to evaluate the performance of the proposed EWS models in predicting Indonesian currency crises.
152
to the previous studies in this field where the performance of predictive models
across different prediction horizons have never been compared.
The discussion in this chapter will be organised as follows. Section 7.2 compares
the prediction results of the models for the 24-month crisis window. Section 7.3
conducts the sensitivity analysis for these models across the shorter crisis
windows. Section 7.4 compares the prediction results of these models across the
shorter crisis windows. Section 7.5 presents the concluding remarks.
7.2. Modelling Results Using a 24-month Crisis Window
Although these models have their advantages and disadvantages over each
other, this section of the study evaluates and compares the performance of these
models in order to determine the best model for predicting the currency crises
in Indonesia. Towards that goal, this study first compares their in-sample
predictions, followed by an evaluation of their out-of-sample predictions.
7.2.1. In-Sample Predictions Using a 24-month Crisis Window
This section evaluates the performance of three EWS models by comparing the
in-sample predictions. Figure 7.1 presents their in-sample probability of a crisis
from 1970/1971 to 1995. In this figure, the currency crises will be determined
whenever the probability of crises shifts across the cut-off probability (Pr*) or
threshold. For this purpose, four cut-off probabilities are set, namely 20%, 30%,
40% and 50%. In addition, Table 7.1 presents a comparison of the in-sample
assessment results for each model based on these thresholds.
Based on Figure 7.1, these models are generally able to predict the three in-
sample currency crises, with their respective probability of a crisis increasing
during the 24 months prior to these currency crises. However, in comparing the
results, this figure indicates that the ANN model was superior to the other two
models, namely the signal and probit models, as it was able to capture the
entire period of 24 months prior to these crises.
FIGURE 7.1
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FIGURE 7.1 In-Sample Prediction Using a 24-month Crisis Window
(a) The signal model
(b) The probit model
(c) The ANN model
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This result is also supported by the percentage of pre-crisis periods correctly
called reported in Table 7.1. This assessment method indicates that the ANN
model is able to predict 99% at Pr*=50% and 100% at Pr*=20%. On the other
hand, the probit and signal models were only able to predict 75% and 57% at
Pr*=50% and 94% and 83% at Pr*=20%, respectively.
In addition, Figure 7.1 also indicates that the signal model has less ability to
capture the tranquil periods because it sends more false alarms than other
models. This is because its warning signals are not followed by any currency
crises within 24 months. The finding is also supported by the percentage of false
alarms sent by these models, with the signal model sending more false alarms
than the probit and ANN models for all Pr*. According to Table 7.1, the signal
model is able to predict the tranquil periods at about 74% for the cut-off
probability or Pr*=20%. While the probit and ANN models are able to capture
more than 87% and 100%, respectively. As Pr* increases to 50%, the ability of
these models also increases but the signal model still has less ability than the
other two models (being able to capture 92% (signal model), 94% (probit model)
and 100% (ANN model), respectively).
Furthermore, we assess the overall performance of the model against all
observations including the pre-crisis and tranquil periods for the entire in-
sample periods, Table 7.1 verifies that the ANN model performed better than
the other models, as it was able to capture 98% at Pr*=20% and almost 100% at
Pr*=50%, while the probit and signal models captured 89% and 77% at Pr*=20%,
and 89% and 83% at Pr*=50%, respectively. Finally, the scores of QPS and GSB
in Table 7.1 also indicate that these models generally perform well in predicting
the in-sample currency crises in Indonesia, for their score is close to zero, which
represents perfectly accurate prediction and calibration. However, as with the
other assessment methods, these methods also indicate that the ANN model is
superior when compared to the other models, as its scores are the lowest
recorded.
155
TABLE 7.1 In-sample Evaluation Using a 24-month Crisis Window
Pr* Assessment methods In-sample* (1970/71-1995)
Signal Probit ANN
20%
% of observations correctly called 76.53% 88.67% 98.33%
% of pre-crisis periods correctly called 82.90% 94.44% 100.00%
% of tranquil periods correctly called 74.47% 86.84% 97.81%
% of false alarms of total alarms 48.78% 30.61% 6.49%
QPS 0.4695 0.2267 0.0333
GSB 0.0457 0.0150 0.0006
30%
% of observations correctly called 76.53% 90.00% 99.67%
% of pre-crisis periods correctly called 82.90% 88.89% 100.00%
% of tranquil periods correctly called 74.47% 90.35% 99.56%
% of false alarms of total alarms 48.78% 25.58% 1.37%
QPS 0.4695 0.2000 0.0067
GSB 0.0457 0.0044 0.0000
40%
% of observations correctly called 83.28% 91.33% 99.67%
% of pre-crisis periods correctly called 56.58% 87.50% 98.61%
% of tranquil periods correctly called 91.92% 92.54% 100.00%
% of false alarms of total alarms 30.65% 21.25% 0.00%
QPS 0.3344 0.1733 0.0067
GSB 0.0041 0.0014 0.0000
50%
% of observations correctly called 83.28% 89.33% 99.67%
% of pre-crisis periods correctly called 56.58% 75.00% 98.61%
% of tranquil periods correctly called 91.92% 93.86% 100.00%
% of false alarms of total alarms 30.65% 20.59% 0.00%
QPS 0.3344 0.2133 0.0067
GSB 0.0041 0.0004 0.0000 * The performance assessment for signal is based on January 1970 to December 1995, while the two
models, namely probit and ANN models are based on January 1971 to December 1995
7.2.2. Out-of-Sample Prediction Using a 24-month Crisis Window
This section evaluates the performance of the three EWS models that have
been developed in previous chapters by comparing their out-of-sample
prediction results. For this purpose, Figure 7.2 shows the probability of a
crisis for each model during the period January 1996 to September 2008 in
addition to a comparison of the assessment results based on four cut-off
probabilities as shown in Table 7.2.
FIGURE 7.2 Out-of-Sample Prediction
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ample Prediction Using a 24-month Crisis Window
(a) The signal model
(b) The probit model
(c) The ANN model
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As previously noted, the currency crisis that occurred in this period was
only one, namely, the Asian Financial Crisis in 1997/98; therefore, all these
EWS models will be evaluated based on their ability to predict this crisis.
Figure 7.2 shows that in general these three models were capable of doing
so, for their probability of a crisis increased within the period of a crisis.
However, their ability to capture the entire pre-crisis episode, which is
indicated by the yellow shaded area in Figure 7.2, varied greatly among
them.
Table 7.2 also shows that all models were able to predict this crisis.
Nevertheless, the ANN model performed better than the other two models,
as it was able to predict 90% at Pr*=50% to 97% at Pr*=20% of the pre-crisis
periods. While the signal and probit models could only predict 73% and
53% of the pre-crisis periods at Pr*=20%, when the cut-off probability
increased to 50%, the predictive capabilities of both models fell to 30% and
43%, respectively.
With regard to the timing of warning signals transmitted, Figure 7.2 shows
that the ANN model sent warning signals of the Asian Financial Crisis from
January 1996, with the probability of Indonesia having this crisis within 24
months being about 66%. The signal model was also able to predict the
existence of this crisis from April 1996, with probability of a crisis being
36%. Unlike the other two models, the probit model started to send warning
signals late in January 1997, with the probability of a crisis of about 66%.
In addition, although there was no currency crisis occurring after the Asian
Financial Crisis of 1997/98, Figure 7.2 clearly shows that the three models
were still sending lots of warning signals. These can be interpreted as false
alarms because their probability of a crisis passed their cut-off probabilities,
but no currency crisis occurred within 24 months after these signals were
received. Based on Table 7.2, at Pr*=20% the probit model sent more false
alarms than the signal and ANN models. However when Pr* increased to
50%, the signal model sent slightly more warnings than the probit model,
158
that is, 77% vs. 76%, while the ANN model had the lowest percentage of
false alarms, 56%.
TABLE 7.2 Out-of-Sample Evaluation Using a 24-month Crisis Window
Pr* Assessment methods
Out-of-sample
1996-1998 1996-2008
Signal Probit ANN Signal Probit ANN
20%
% of observations correctly called 77.78% 44.44% 88.89% 43.14% 48.37% 69.28%
% of pre-crisis periods correctly called 73.33% 53.33% 96.67% 73.33% 53.33% 96.67%
% of tranquil periods correctly called 100.00% 0.00% 50.00% 35.77% 47.15% 62.60%
% of false alarms of total alarms 0.00% 27.27% 9.38% 78.22% 80.25% 61.33%
QPS 0.4444 1.1111 0.2222 1.1373 1.0327 0.6144
GSB 0.0988 0.0988 0.0062 0.4307 0.2222 0.1730
30%
% of observations correctly called 77.78% 38.89% 86.11% 43.14% 52.94% 71.90%
% of pre-crisis periods correctly called 73.33% 46.67% 93.33% 73.33% 46.67% 93.33%
% of tranquil periods correctly called 100.00% 0.00% 50.00% 35.77% 54.47% 66.67%
% of false alarms of total alarms 0.00% 30.00% 9.68% 78.22% 80.00% 59.42%
QPS 0.4444 1.2222 0.2778 1.1373 0.9412 0.5621
GSB 0.0988 0.1543 0.0015 0.4307 0.1367 0.1300
40%
% of observations correctly called 41.67% 38.89% 86.11% 66.67% 56.21% 74.51%
% of pre-crisis periods correctly called 30.00% 46.67% 93.33% 30.00% 46.67% 93.33%
% of tranquil periods correctly called 100.00% 0.00% 50.00% 75.61% 58.54% 69.92%
% of false alarms of total alarms 0.00% 30.00% 9.68% 76.92% 78.46% 56.92%
QPS 1.1667 1.2222 0.2778 0.6667 0.8758 0.5098
GSB 0.6806 0.1543 0.0015 0.0069 0.1047 0.1047
50%
% of observations correctly called 41.67% 36.11% 83.33% 66.67% 60.78% 75.82%
% of pre-crisis periods correctly called 30.00% 43.33% 90.00% 30.00% 43.33% 90.00%
% of tranquil periods correctly called 100.00% 0.00% 50.00% 75.61% 65.04% 72.36%
% of false alarms of total alarms 0.00% 31.58% 10.00% 76.92% 76.79% 55.74%
QPS 1.1667 1.2778 0.3333 0.6667 0.7843 0.4837
GSB 0.6806 0.1867 0.0000 0.0069 0.0578 0.0821
These false alarms also decreased the ability of the models to capture the
tranquil period during this period, compared to the in-sample period. For
example, the signal model was only able to capture 36% at Pr*=20% to 77%
at Pr*=50%, the probit model could only predict 47% at Pr*=20% to 65% at
Pr*=50%, while the ANN model was able to capture around 63% at Pr*=20%
to 72% at Pr*=50%. Generally, these three EWS models have proved
themselves in terms of their accuracy and calibration, as indicated by their
QPS and GSB scores being close to zero.
Finally, to conclude this section, it was found that these three EWS models
were able to predict the Indonesian currency crises for both in-sample and
159
out-of-sample periods as shown in Figures 7.1 and 7.2. This predictive
ability was also indicated by the percentage of pre-crisis correctly called in
Tables 7.1 and 7.2. However the ANN model performed better than the
other two models, as it was able to capture 100% of the in-sample pre-crisis
periods and 90% at Pr*=50% to 97% at Pr*=20% for the out-of-sample pre-
crisis periods. This positive picture is also supported by other assessment
methods covered in Tables 7.1 and 7.2.
The next section will explore the sensitivity of these three EWS models by
assessing their consistency in predicting Indonesian currency crises with
shorter crisis windows, namely 6, 12 and 18 months.
7.3. The Sensitivity Tests for Shorter Crisis Windows
Like previous studies by Kaminsky and Reinhart (1999), Goldstein et al. (2000),
Zhuang and Dowling (2002), Edison (2000) and so on, this study utilises three
EWS models to predict the Indonesian currency crises using a 24-month crisis
window. However, following Kaminsky (1999) and Goldstein et al. (2000),
which did the sensitivity test for their signal model using shorter crisis
windows of 12 and 18 months, this study analyses the sensitivity and
consistency of these three models in predicting currency crises within shorter
crisis windows of 6, 12, and 18 month durations.
Although these models predict the Indonesian currency crises using these
shorter crisis windows, for consistency and comparison, the same data set will
be used for both in-samples and out-of-samples. For example, except for the
signal model which uses the in-sample data from 1970 to 1995, other models,
namely the probit and ANN models, use the in-sample data from 1971 to 1995,
while the same out-of-sample data set from 1996 to 2008 is used. To assess the
sensitivity and consistency of these models, an attempt is made to modify and
re-estimate the signal model, followed by similar treatment for the probit and
ANN models.
160
7.3.1 The Signal model
This subsection analyses the sensitivity and consistency of the signal model in
predicting the currency crises in Indonesia using three crisis windows. In re-
estimating this signal model, two options related to determine the noise-to-
signal ratio (NSR) for each leading indicators are adopted. NSR plays a vital
role here in choosing the set of leading indicators to be used and to assign
weights to each indicator to form the composite index. In the first option, this
study applies the benchmark signal model that was developed in Chapter 4 and
uses the same noise-to-signal ratio (NSR) for all leading indicators with the
benchmark model. This model adjusts its crisis window with new shorter crisis
windows to accommodate these changes. This model can be described as the
signal model with a fixed NSR. In contrast, for the second option, similar
procedures are adopted with the benchmark model to recalculate the lowest
NSR for each leading indicator based on their ability to predict the crisis within
these new crisis windows and is later called the signal model with adjusted
NSR.
For this purpose, this study re-estimates the signal model that was discussed in
Chapter 4 for various shorter crisis windows by developing three new signal
models dedicated for each crisis window. For example, signal 1 is the signal
model for predicting the currency crises using a 6-month crisis window, signal
2 is the signal model for predicting the currency crises using a 12- month crisis
window, while signal 3 is the signal model dedicated to predict the currency
crises using a 18-month crisis window. However, to distinguish between these
two options of signal models, the symbols “a” that refer to option 1 and “b” for
option 2, are used.
Following an evaluation of the performance of these models in predicting the
Indonesian currency crises for each crisis window, the performance will be
compared with the benchmark signal model. This will be done by using the 24-
month crisis window to assess the sensitivity and consistency of this approach
when the assumption of crisis windows changes.
161
The Signal Model with Fixed NSR
As a consequence of using the same NSR with the benchmark model, this
model uses the same number and list of variables as the benchmark model
developed in Chapter 4. Furthermore, the composite index held by each new
model is also the same as the benchmark model. However, the difference
between the benchmark model and these new models lies in calculating the
probability of a crisis using Equation 4.3. As mentioned previously, this study
found that there are three in-sample currency crises in Indonesia from 1970 to
1995 when using Equation 3.2. Therefore, when calculating the probability of a
crisis using Equation 4.3, these three new signal models consider a fewer
number of months in the period of crisis compared to the benchmark model
which leads to differences in the probability of a crisis for each model. For
example, as the benchmark model uses the 24-month crisis window, the
number of months in its crisis period is 72 months. On the other hand, signal 1a,
which predicts a crisis within the 6-month crisis window, only has 18 months,
and signal 2a, which uses a 12-month crisis window, has 36 months. Signal 3a,
using the 18-month crisis window, has only 54 months.
Figure 7.3 shows the probability of a crisis for these three new models in
predicting the in-sample currency crises, while Figure 7.4 presents their
probability of a crisis in predicting the out-of-sample currency crisis. In
addition, the in-sample performance assessment for these models is presented
in Table 7.3, while Table 7.4 presents the out-of-sample performance assessment
for these models.
The Signal Model with Fixed NSR: In-Sample Prediction
Figure 7.3 reveals that these signal models do not perform very well, being only
able to predict two of the three in-sample currency crises, with their probability
of a crisis only increasing during the first two pre-currency crisis periods. In the
last in-sample the crisis in September 1986 could not be predicted using these
models, their probability of a crisis remaining low during this pre-crisis period.
162
FIGURE 7.3 The Signal Model with Fixed NSR’s In-Sample Prediction
(a) A 6-month crisis window (signal 1a)
(b) A 12-month crisis window (signal 2a)
(c) A 18-month crisis window (signal 3a)
0%
20%
40%
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80%
100%1
97
1M
01
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72
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92
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93
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94
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1
19
95
M0
1
cc_6m Fixed NSR
0%
20%
40%
60%
80%
100%
19
71
M0
1
19
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M0
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19
94
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95
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cc_12m Fixed NSR
0%
20%
40%
60%
80%
100%
19
71
M0
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72
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19
89
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19
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91
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19
92
M0
1
19
93
M0
1
19
94
M0
1
19
95
M0
1
cc_18m Fixed NSR
163
For example, they show the probability of a crisis on average during this pre-
crisis period is not significant, as shown by the figures of 7% (signal 1a), 10%
(signal 2a) and 17% (signal 3a). Figure 7.3 also shows that compared to the other
crisis windows, the ability of the signal model in predicting a crisis within the 6-
months crisis window (signal 1a) is more limited, its maximum probability of a
crisis being only 32%. Thus if the cut-off probability (Pr*) increases to 40% or
more, this model cannot send any warning signals because its probability of a
crisis is lower than this threshold. It is also indicated by the percentage of the
pre-crisis period accurately predicted by these models in Table 7.3.
TABLE 7.3 The Signal Model with Fixed NSR’s In-Sample Evaluation
Pr* Assessment methods In-sample (1970-1995)
6m 12m 18m 24m
20%
% of observations correctly called 90.68% 91.96% 85.21% 76.53%
% of pre-crisis periods correctly called 55.56% 58.33% 64.81% 82.90%
% of tranquil periods correctly called 92.83% 96.36% 89.49% 74.47%
% of false alarms of total alarms 67.74% 32.26% 43.55% 48.78%
QPS 0.1865 0.1608 0.2958 0.4695
GSB 0.0035 0.0005 0.0013 0.0457
30%
% of observations correctly called 90.68% 91.96% 85.21% 76.53%
% of pre-crisis periods correctly called 55.56% 58.33% 64.81% 82.90%
% of tranquil periods correctly called 92.83% 96.36% 89.49% 74.47%
% of false alarms of total alarms 67.74% 32.26% 43.55% 48.78%
QPS 0.1865 0.1608 0.2958 0.4695
GSB 0.0035 0.0005 0.0013 0.0457
40%
% of observations correctly called 94.21% 91.96% 88.75% 83.28%
% of pre-crisis periods correctly called 0.00% 58.33% 46.30% 56.58%
% of tranquil periods correctly called 100.00% 96.36% 97.67% 91.92%
% of false alarms of total alarms NA 32.26% 19.35% 30.65%
QPS 0.1158 0.1608 0.2251 0.3344
GSB 0.0067 0.0005 0.0109 0.0041
50%
% of observations correctly called 94.21% 91.96% 88.75% 83.28%
% of pre-crisis periods correctly called 0.00% 58.33% 46.30% 56.58%
% of tranquil periods correctly called 100.00% 96.36% 97.67% 91.92%
% of false alarms of total alarms NA 32.26% 19.35% 30.65%
QPS 0.1158 0.1608 0.2251 0.3344
GSB 0.0067 0.0005 0.0109 0.0041
Note: Pr*: the cut-off probability
Based on Table 7.3, at Pr*=20%, these models can only predict about 56% (for
signal 1a), 58% (for signal 2a) and 65% (signal 3a). However, when Pr* increases,
except for signal 2a, the ability of the other models in predicting crises falls. For
example, when Pr*=50%, the capacity of signal 3a drops to 46%, and signal 1a
164
cannot capture any currency crises at all. Likewise, the benchmark model is able
to predict crises by 83% at Pr*=20%, but when Pr* increases to 50%, its ability
declines to 57%, or is slightly lower than signal 2a.
In contrast, the ability of these models in capturing tranquil periods increases in
line with the increase in Pr*. For example, the ability of these models in
capturing the tranquil periods tends to increase with the increase in the cut-off-
probability. When Pr*=20%, these models are able to capture the tranquil
periods around 90%, thus much higher than the benchmark model.
Furthermore, when Pr* increases to 50%, the ability of these models also
increase to 100% (signal 1a), 96% (signal 2a) and 98% (signal 3). Again, these
figures are much higher than that of the benchmark model. Similar results also
apply when predicting all observations, in addition to the level of accuracy and
calibration of these models, which are represented by low scores of QPS and
GSB recorded in Table 7.3. Conversely, the percentage of false alarms relative to
total alarms from these models tends to fall in line with the increasing ability of
the model in capturing the tranquil period and the cut-off probability.
The Signal Model with Fixed NSR: Out-of-Sample Prediction
Figure 7.4 presents the ability of these models to predict the out-of-sample
crises from 1996 to 2008, while the performance assessment of these models is
shown in Table 7.4. Based on Figure 7.4, these models are generally able to
capture the Asian Financial Crisis, as their probability of a crisis increases
within their crisis windows, as indicated by the yellow shaded area. However
their performance is limited, for they are unable to capture the entire pre-crisis
periods.
165
FIGURE 7.4 The Signal Model with Fixed NSR’s Out-of-Sample prediction
(a) A 6-month crisis window (signal 1a)
(b) A 12-month crisis window (signal 2a)
(c) A 18-month crisis window (signal 3a)
0%
20%
40%
60%
80%
100%
19
96
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11
99
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19
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cc_6m Fixed NSR
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166
The finding is also supported by Table 7.4, which indicates that signal 3a is able
to predict 50% of its pre-crisis periods, while the other two models fail to
perform as well because they are only able to cover 19% (signal 1a) and 23%
(signal 2a) of pre-crisis periods at Pr*=20%. However, when Pr* increases to
50%, except for signal 2a, the ability of these models to capture their pre-crisis
widows declines. For example, the pre-crisis prediction results for signal 3a
drop to 18%, while signal 1a is unable to send any warning signals, as its
probability of a crisis is less than the cut-off probability.
The results show that the performance of the benchmark model is still better
than those models with shorter crisis windows. In other words, for the signal
model, using shorter crisis windows tends to degrade the ability of this model
to predict currency crises in Indonesia. Moreover, Figure 7.4 shows that these
models also send several false alarms, the numbers tending to increase along
with the longer prediction horizon or crisis window. In Table 7.4, during crisis
periods, except for signal 1a, no model sends out false alarms. However, for the
entire out-of-samples, at Pr*=20%, signal 2a sends less false alarms than the
other models, which send more than 50%. Furthermore when Pr* is increased,
the number of their false alarms declined. For example, at Pr*=50%, except for
signal 1a, only 29% of signals transmitted by signals 2a and 3a could be
categorized as false alarms. This table indicates that these models perform well
compared to the benchmark model. The number of false alarms also determines
the ability of the model to capture the tranquil periods. As these models send
fewer false signals than the benchmark model, so the ability of these models in
capturing the tranquil period is also stronger than the benchmark model.
16
7
TA
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It is also evident that the number of observations can be predicted accurately by
these models, thus performing better than the benchmark model. Likewise, the
accuracy and calibration are good, as the QPS and GSB scores are low. This
result is not surprising because the tranquil period is much longer than the
period of crisis, so although the benchmark model is able to better predict the
pre-crisis period, it is less able to capture the tranquil periods because its
probability of a crisis is more volatile than that of the signal models with their
shorter crisis windows.
The Signal Model with Adjusted NSR
As previously mentioned, in this second scenario, the same steps in developing
the signal model as used in Chapter 4 are applied. Similar to the benchmark
model, this study uses 55 leading indicators and transforms them into dummy
warning signals or sit=1 whenever they pass their thresholds, otherwise there
are no signals, or sit=0. Following Table 4.1 in Chapter 4, these warning signals
can be classified into four groups based on their ability to predict the currency
crises within specific crisis windows. Based on this classification of warning
signals, the lowest noise-to-signal ratio (NSR) is calculated for each leading
indicator. This is done using Equation 4.1 by adjusting the threshold and
ranking these leading indicators based on the lowest NSR. This represents the
best indicator to predict the currency crisis within specific crisis windows. The
results of which are presented in Table 7.5.
Similar to the benchmark approach in Chapter 4, this study also selects the set
of leading indicators based on their NSR being less than unity, or NSR<1. Due
to using different crisis windows, Table 7.5 shows that the number of leading
indicators used for constructing a composite index and the rank of best leading
indicators based on the lowest score of NSR, also changed. For example, unlike
the benchmark model in Chapter 4 that used 39 of 55 leading indicators, the
signal model for the 6 months crisis window (or signal 1b) used 40 of 55 leading
indicators, while both signal models using 12 months (or signal 2b) and 18
months (or signal 3b) crisis windows used 37 of 55 leading indicators.
169
TABLE 7.5 List Indicator Based on NSR for Various Crisis Windows
No LEADING INDICATORS 6 m 12 m 18 m 24 m
NSR R NSR R NSR R NSR R
1 Real US$/yen exchange rate1 0.25 11 0.02 2 0.04 3 0.03 1
2 Short-term capital flows to GDP 0.08 3 0.01 1 0.02 1 0.03 2
3 Current account balance to GDP 0.11 4 0.1 5 0.02 2 0.04 3
4 US annual growth rate 0.4 21 0.11 6 0.04 4 0.06 4
5 US real interest rate3 0.8 38 1.14 41 1.69 42 0.09 5
6 Short -term capital flows to GDP3 0.19 7 0.05 3 0.08 5 0.12 6
7 US real interest rate 0.44 25 0.41 21 0.25 13 0.13 7
8 Loans to deposits3 0.58 30 1.15 42 0.41 21 0.23 8
9 M1 to GDP3 1.75 42 0.27 17 0.15 7 0.23 9
10 Real effective exchange rate1 0.12 6 0.07 4 0.11 6 0.24 10
11 Domestic real interest rate3 NA 50 NA 50 NA 50 0.26 11
12 Exports2 0.26 14 0.24 13 0.22 11 0.29 12
13 M1 to GDP 0.55 29 0.13 7 0.21 8 0.32 13
14 Government consumption to GDP 0.23 9 0.35 20 0.3 16 0.33 14
15 Foreign reserves in months of imports
0.06 1 0.14 8 0.22 9 0.34 15
16 Trade balance to GDP3 0.3 16 0.41 22 0.4 20 0.34 16
17 Foreign reserves2 0.19 8 0.15 9 0.22 10 0.34 17
18 Foreign reserves in months of imports3
0.32 18 0.21 11 0.38 19 0.36 18
19 Government consumption to GDP3 0.9 40 0.58 30 0.42 24 0.38 19
20 Domestic real interest rate differential from US rate3
NA 47 NA 47 NA 47 0.38 20
21 Lending to deposit rate spread 0.25 12 0.24 14 0.24 12 0.39 21
22 Current account balance to GDP3 0.39 20 0.5 24 0.74 34 0.4 22
23 Deposits to M23 0.26 13 0.55 29 0.48 25 0.4 23
24 Net credit to government to GDP3 0.07 2 0.15 10 0.25 14 0.41 24
25 Fiscal balance to GDP3 0.32 17 0.29 18 0.26 15 0.43 25
26 Deposits in BIS banks to reserves3 NA 46 NA 46 NA 46 0.44 26
27 Real exchange rate against US$1 0.68 34 0.58 31 0.53 27 0.52 27
28 Domestic real interest rate 0.41 22 0.35 19 0.35 17 0.57 28
29 M2 to reserves3 0.28 15 0.23 12 0.37 18 0.58 29
30 Fiscal balance to GDP 0.25 10 0.26 15 0.42 23 0.6 30
31 M2 multiplier2 0.5 27 0.52 25 0.69 33 0.62 31
32 M2 multiplier 0.65 31 0.54 27 0.57 28 0.62 32
33 Oil price 0.78 37 0.73 35 0.64 31 0.62 33
34 M2 to reserves 0.12 5 0.26 16 0.42 22 0.65 34
35 Domestic credit to GDP3 0.78 35 0.68 34 0.65 32 0.73 35
36 Central bank credit to the public sector to GDP
0.65 32 0.63 33 0.64 30 0.74 36
37 Domestic real interest rate differential from US rate
0.44 24 0.52 26 0.5 26 0.82 37
38 Real commercial bank deposits2 0.42 23 0.44 23 0.62 29 0.95 38
39 Trade balance to GDP 0.37 19 0.6 32 0.8 35 0.99 39
40 Short -term external debt to reserves3
0.51 28 0.84 36 0.98 37 1 40
170
TABLE 7.5 List Indicators Based on NSR for Various Crisis Windows (Continued)
No LEADING INDICATORS 6 m 12 m 18 m 24 m
NSR R NSR R NSR R NSR R
41 Foreign liabilities to foreign assets3 0.78 36 1.11 39 1.29 40 1.3 41
42 Domestic credit to GDP 1.02 41 1.11 40 1.2 38 1.35 42
43 Net credit to government to GDP 0.67 33 1.07 38 1.25 39 1.64 43
44 Central bank credit to the public sector to GDP3
0.48 26 0.87 37 1.41 41 1.67 44
45 Short-term external debt to reserves NA 43 NA 43 NA 43 1.7 45
46 Imports2 0.9 39 0.55 28 0.88 36 1.84 46
47 Stock price index in local currency2 NA 53 NA 53 NA 53 1.95 47
48 Foreign liabilities to foreign assets NA 44 NA 44 NA 44 3.96 48
49 Loans to deposits NA 48 NA 48 NA 48 6.14 49
50 Deposits in BIS banks to reserves NA 45 NA 45 NA 45 NA 50
51 Deposits to M2 NA 49 NA 49 NA 49 NA 51
52 Lending ]deposit rate spread3 NA 51 NA 51 NA 51 NA 52
53 Oil price2 NA 52 NA 52 NA 52 NA 53
54 Industrial/manufacturing production index2
NA 54 NA 54 NA 54 NA 54
55 Domestic consumer price index2 NA 55 NA 55 NA 55 NA 55
# of leading indicator used in the model 40 37 37 39
# of leading indicator excluded in the model
15 18 18 16
Spearman’s rank correlation coefficient relative to the benchmark model
0.608 0.709 0.791
Note: 1 deviation from trend-HP filter; 2 12m% change; 312m change; NA: not available, R: Rank of indicators based on the lowest NSR
In addition, depending on the rank of the best leading indicators used for each
crisis window, and using the Spearman rank correlation coefficient2, this study
finds that there is significant positive correlation between the rank of the
leading indicator and the crisis windows, because the score of the Spearman
rank correlation increases in line with an increase in the crisis window. For
example, the Spearman correlation rank for the 6 months crisis window is 0.6;
however, when the crisis window is extended into 12 and 18 months, the
Spearman rank correlation also increases to 0.7 and 0.8.
Furthermore, the composite index using the selected leading indicators is
generated. However, as the ability of each indicator to predict the crisis varies
the benchmark approach is adopted. Following Equation 4.2, the inverse of
NSR is employed as the weight of the signals of these selected leading
2 Spearman’s rank correlation coefficient can be used to identify the strength of correlation between two variables, and see whether the correlation is positive or negative. This coefficient ranges from -1 to 1 and value of -1 means perfectly negative correlation, if its value is zero it represents no correlation and if the value is 1 it means perfectly positive correlation.
171
indicators. As indicators with a low NSR refer to better indicators in predicting
a crisis, they contribute more in developing the composite index than indicators
with higher NSR. Finally, using Equation 4.3, this composite index can be
transformed into the model’s probability of a crisis. The models’ probabilities of
a crisis for each crisis window are presented in Figures 7.5 and 7.6. These cover
(a) Signal model with adjusted NSR for 6-month crisis windows, or signal 1b,
(b) Signal model with adjusted NSR for 12-month crisis window, or signal 2b,
and (c) Signal model with adjusted NSR for 18-month crisis window, or signal
3b. In addition, the performance assessments of these models for all crisis
windows are presented in Tables 7.6 and 7.7.
The Signal Model with Adjusted NSR: In-Sample Prediction
Figure 7.5 indicates that these signal models are able to predict three in-sample
currency crises within three shortened crisis windows as their probability of a
crisis increases during the pre-crisis periods. It is also supported by the
percentage of pre-crisis periods recorded in Table 7.6 where these three models
can capture 78% (signal 1b), 83% (signal 2b) and 80% (signal 3b) at Pr*=20%.
However, the ability of signal 1b in predicting these in-sample crises within the
6-month crisis windows is more limited that of other models. Even though, the
probability of a crisis of signal 1b increases during all the pre-crisis periods, its
maximum probability of a crisis is 45%. Thus, if Pr* then increases to 50%, signal
1b fails to send any warning signals.
In contrast, even though Pr* increases to 50%, the other models can still capture
the pre-crisis periods by 58% (signal 2b) and 46% (signal 3b). In comparison
with the benchmark model developed in Chapter 4, which is able to capture
pre-crisis periods by 83% at Pr*=20% but decreases to 57% when Pr* increases to
50%, with the exception of signal 1b, it was found that this model is not so
sensitive to the change in crisis windows.
FIGURE 7.5 The Signal M
(a) A 6-month crisis window
(b) A 12-month crisis window
(c) A 18-month crisis window
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Model with Adjusted NSR’s In-Sample Prediction
month crisis window (Signal 1b)
month crisis window (Signal 2b)
month crisis window (Signal 3b)
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This is because it is consistently able to predict the pre-crisis period within
shortened crisis windows for all cut-off probabilities, while signal 2b performs
better than the benchmark model. Moreover, Figure 7.5 indicates that the ability
of these models to predict the third in-sample crisis is also limited, as the
models’ probability of a crisis for this crisis across the crisis windows ranges
from 30% (signal 2b) to 45% (signal 1b), so when Pr* increases to 50%, these
models cannot send any warning alarms for this crisis episode.
In terms of false signals, and based on Figure 7.5, this study indicates that these
models also sent more false alarms, particularly at the beginning of the 1970s
and late 1980s to early 1990s, though these are not significant. It is also
supported by the percentage of false alarms in Table 7.6, which indicates all
these models sent false alarms around 50% at Pr*=20%. However, when Pr* was
increased to 50%, the number of false alarms decreased to 30% (signal 2b), and
19% (signal 3b), while for predicting a crisis within a 6-month crisis window,
signal 1b sent no false alarms because its maximum probability of a crisis was
45%, or less than the cut-off probability of 50% (Pr*=50%).
In capturing the tranquil period, this study also found that the prediction
results of these models for shorter crisis windows also performed well and were
an improvement over the benchmark model. It is also supported by the ability
to capture all observations including the pre-crisis periods and tranquil periods.
Furthermore, Table 7.6 also indicates that these models perform well in
predicting the pre-in-sample currency crisis periods for these crisis windows, as
shown by low QPS and GSB scores, which are close to zero, thus indicating
perfect accuracy and calibration. In addition, they are also better than the
benchmark model, as their scores are slightly lower.
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TABLE 7.6 The Signal Model with Adjusted NSR’s In-Sample Evaluation
Pr* Assessment methods In-sample (1970-1995)
6m 12m 18m 24m
20%
% of observations correctly called 93.25% 87.78% 80.71% 76.53%
% of pre-crisis periods correctly called 77.78% 83.33% 79.63% 82.90%
% of tranquil periods correctly called 94.20% 88.36% 80.93% 74.47%
% of false alarms of total alarms 54.84% 51.61% 53.26% 48.78%
QPS 0.1350 0.2444 0.3859 0.4695
GSB 0.0035 0.0140 0.0299 0.0457
30%
% of observations correctly called 93.25% 91.96% 85.85% 76.53%
% of pre-crisis periods correctly called 77.78% 58.33% 66.67% 82.90%
% of tranquil periods correctly called 94.20% 96.36% 89.88% 74.47%
% of false alarms of total alarms 54.84% 32.26% 41.94% 48.78%
QPS 0.1350 0.1608 0.2830 0.4695
GSB 0.0035 0.0005 0.0013 0.0457
40%
% of observations correctly called 93.25% 91.96% 88.75% 83.28%
% of pre-crisis periods correctly called 77.78% 58.33% 46.30% 56.58%
% of tranquil periods correctly called 94.20% 96.36% 97.67% 91.92%
% of false alarms of total alarms 54.839% 32.26% 19.35% 30.65%
QPS 0.1350 0.1608 0.2251 0.3344
GSB 0.0035 0.0005 0.0109 0.0041
50%
% of observations correctly called 94.21% 91.96% 88.75% 83.28%
% of pre-crisis periods correctly called 0.00% 58.33% 46.30% 56.58%
% of tranquil periods correctly called 100.00% 96.36% 97.67% 91.92%
% of false alarms of total alarms 0.00% 32.26% 19.35% 30.65%
QPS 0.1158 0.1608 0.2251 0.3344
GSB 0.0067 0.0005 0.0109 0.0041
Note: Pr*: the cut-off probability
The Signal Model with Adjusted NSR: Out-of-Sample Prediction
Figure 7.6 presents the results of the out-of-sample predictions for the signal
model for all crisis windows and the performance evaluation results across the
crisis windows. The cut-off probabilities are presented in Table 7.7. Similar to
the in-sample predictions, in Figure 7.6, the models are able to predict the out-
of-sample crises at all prediction horizons, as their probability of a crisis
increased during the pre-crisis periods.
FIGURE 7.6 The
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The Signal Model with Adjusted NSR’s Out-of
(a) A 6-month crisis window (signal 1b)
(b) A 12-month crisis window (signal 2b)
(c) A 18-month crisis window (signal 3b)
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(signal 2b)
(signal 3b)
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It was also demonstrated by the percentage of pre-crisis periods correctly called
in Table 7.7. Based on this method, these models can capture 50% (signal 1b),
64% (signal 2b) and 57% (signal 3b) at Pr*=20%. As with the in-sample
predictions, the ability of signal 1b is more limited than that of other models
because its maximum probability of a crisis is only 45%, so when Pr* increased
to 50% this model was unable to capture any crises. Furthermore, for other
models, if Pr* increases to 50%, they are still able to capture this pre-crisis
period, but unlike the benchmark model, which is able to predict 30% of pre-
crisis periods, the prediction results decrease and are insignificant, as they can
only capture 18% (signal 2b) and 11% (signal 3b). For more details, see Table
7.7.
In terms of the timing of warning signals sent by these models, with the
exception of signal 1b, signal 2b sends warning signals quite late. After
December 1996 its probability of a crisis was not significant, being at 29%. On
the other hand, signal 3b warned of the presence of crisis earlier in 1996,
although its probability of a crisis remained low at 16.1%. Moreover signal 2b
sent significant warning signals with the probability of Indonesia having a crisis
being 68% within 12 months after December 1997. Similarly, in predicting the
18-month pre-crisis period, signal 3b sent warning signals after December 1996
with the probability of a crisis being 36%. Following this, it fluctuated around
16% to 36%, before its probability of a crisis increased to 81% in January 1998
where it remained for 3 months. It then dropped to 36% in April 1998 and
recorded a further drop to 16% in June 1998. This occurred one month after the
resignation of Soeharto as the second President of the Republic of Indonesia.
Furthermore, if Pr*=50%, signal 1b failed to send any warning signals but the
other models, even though too late, still warned the government of the Republic
of Indonesia of the presence of the Asian Financial Crisis in December 1997
(signal 2b), or January 1998 (signal 3b). In contrast, the benchmark model was
able to send warning signals reaching 84% in January 1997.
With respect to the number of false alarms sent by these models during the
crisis period from 1996 to 1998, unlike the benchmark model, which sent no
177
false alarms because it predicted the whole of the tranquil periods during this
period, these models with shorter crisis windows sent false alarms about 11%
(signal 1b), 30% (signal 2b) and 6% (signal 3b) at Pr*=20%. In line with the
increase in Pr*, the models’ percentage of false alarms also decreased, as for
example at Pr* =50%, where all models sent no false alarms. It can also be seen
in the ability of these models to capture the tranquil periods during the period
1996 to 1998 when there was an increase in line with increase in Pr*. For
example, when Pr*=50%, these models captured 100% of the tranquil periods.
For the entire out-of-sample period from 1996 to 2008, as there was no currency
crisis defined after the Asian Financial Crisis in 1997/98, all warning signals
after this can be classified as false alarms. As shown in Figure 7.6, these models
sent many false alarms during this period compared to the in-sample
predictions from 1970 to 1995 shown in Figure 7.5. This finding is supported in
Table 7.7 which shows that at Pr*=20% these models sent more false alarms,
being 60% (signal 1b), 79% (signal 2b) and 80% (signal 3b). However, as Pr*
increased, the percentage of false alarms to total signals in the models declined.
For example, when Pr*=50%, unlike signal 1b, which sent no false alarms, the
other models sent them at 33% (signal 2b) and 84% (signal 3b).
Consequently, these false alarms also determine the ability of models to capture
the tranquil periods. For example, at Pr*=20%, these models were able to
capture 91% (signal 1b), 59% (signal 2b) and 49% (signal 3b). As Pr* increased,
the ability of models to predict tranquil periods also increased, for example at
Pr*=50%, signal 1b was able to capture 100% of the tranquil periods, while
signal 2b and signal 3b were able to capture 98% and 87% respectively. This
proved better than the benchmark model, which was able to capture 76%.
Similar results are also derived using the percentage of observations correctly
called, as well as in term of accuracy and calibration indicated by the low QPS
and GSB scores. For more details see Table 7.7.
17
8
TA
BL
E 7
.7 T
he S
ign
al M
odel
wit
h A
dju
sted
NS
R’s
Ou
t-of
-Sam
ple
Eva
luat
ion
Pr*
Assessm
ent methods
Out-of-sample Prediction
1996-1998
1996-2008
6m
12m
18m
24m
6m
12m
18m
24m
20%
% o
f o
bse
rvat
ion
s co
rrec
tly
cal
led
75
.00%
61
.11%
63
.89%
77
.78%
86
.93%
59
.48%
50
.33%
43
.14%
% o
f p
re-c
risi
s p
erio
ds
corr
ectl
y c
alle
d
50.0
0%
63.6
4%
57.1
4%
73.3
3%
50.0
0%
63.6
4%
57.1
4%
73.3
3%
% o
f tr
anq
uil
per
iod
s co
rrec
tly
cal
led
95
.00%
57
.14%
87
.50%
10
0.00
%
91.2
4%
58.7
8%
48.8
0%
35.7
7%
% o
f fa
lse
alar
ms
of
tota
l al
arm
s 11
.11%
30
.00%
5.
88%
0
.00%
60
.00%
79
.41%
80
.00%
78
.22%
QP
S
0.50
00
0.77
78
0.72
22
0.4
444
0.
2614
0.
8105
0.
9935
1.
1373
GS
B
0.07
56
0.00
62
0.18
67
0.0
988
0.
0014
0.
1808
0.
2310
0.
4307
30%
% o
f o
bse
rvat
ion
s co
rrec
tly
cal
led
75
.00%
50
.00%
58
.33%
77
.78%
86
.93%
86
.93%
62
.75%
43
.14%
% o
f p
re-c
risi
s p
erio
ds
corr
ectl
y c
alle
d
50.0
0%
18.1
8%
46.4
3%
73.3
3%
50.0
0%
18.1
8%
46.4
3%
73.3
3%
% o
f tr
anq
uil
per
iod
s co
rrec
tly
cal
led
95
.00%
10
0.00
%
100.
00%
10
0.00
%
91.2
4%
98.4
7%
66.4
0%
35.7
7%
% o
f fa
lse
alar
ms
of
tota
l al
arm
s 11
.11%
0.
00%
0.
00%
0.
00%
60
.00%
33
.33%
76
.36%
78
.22%
QP
S
0.50
00
1.00
00
0.83
33
0.4
444
0.
2614
0.
2614
0.
7451
1.
1373
GS
B
0.07
56
0.50
00
0.34
72
0.0
988
0.
0014
0.
0219
0.
0623
0.
4307
40
%
% o
f o
bse
rvat
ion
s co
rrec
tly
cal
led
75
.00%
50
.00%
30
.56%
41
.67%
86
.93%
86
.93%
73
.20%
66
.67%
% o
f p
re-c
risi
s p
erio
ds
corr
ectl
y c
alle
d
50.0
0%
18.1
8%
10.7
1%
30.0
0%
50.0
0%
18.1
8%
10.7
1%
30.0
0%
% o
f tr
anq
uil
per
iod
s co
rrec
tly
cal
led
95
.00%
10
0.00
%
100.
00%
10
0.00
%
91.2
4%
98.4
7%
87.2
0%
75.6
1%
% o
f fa
lse
alar
ms
of
tota
l al
arm
s 11
.11%
0.
00%
0.
00%
0.
00%
60
.00%
33
.33%
84
.21%
76
.92%
QP
S
0.50
00
1.00
00
1.38
89
1.1
667
0.
2614
0.
2614
0.
5359
0.
6667
GS
B
0.07
56
0.50
00
0.96
45
0.6
806
0.
0014
0.
0219
0.
0069
0.
0069
50%
% o
f o
bse
rvat
ion
s co
rrec
tly
cal
led
55
.56%
50
.00%
30
.56%
41
.67%
89
.54%
86
.93%
73
.20%
66
.67%
% o
f p
re-c
risi
s p
erio
ds
corr
ectl
y c
alle
d
0.00
%
18.1
8%
10.7
1%
30.0
0%
0.00
%
18.1
8%
10.7
1%
30.0
0%
% o
f tr
anq
uil
per
iod
s co
rrec
tly
cal
led
10
0.00
%
100.
00%
10
0.00
%
100.
00%
10
0.00
%
98.4
7%
87.2
0%
75.6
1%
% o
f fa
lse
alar
ms
of
tota
l al
arm
s 0.
00%
0.
00%
0.
00%
0.
00%
0.
00%
33
.33%
84
.21%
76
.92%
QP
S
0.88
89
1.00
00
1.38
89
1.1
667
0.
2092
0.
2614
0.
5359
0.
6667
GS
B
0.39
51
0.50
00
0.96
45
0.6
806
0.
0219
0.
0219
0.
0069
0.
0069
No
te: P
r*: t
he
Cu
t-o
ff-p
rob
abil
ity
179
As the main objective of the EWS model is to assess the consistency of this
model in being able to predict the episodes of currency crises rather than the
tranquil periods, focus is directed towards the ability of the model to predict the
currency crisis events within specific crisis windows. Furthermore, by applying
this model to predict crises within various shorter crisis windows, this study
can assess the sensitivity and consistency of this model, in addition to finding
the best prediction horizon for predicting Indonesian currency crises.
As a consequence, this study found that for the in-sample prediction, the signal
model was less sensitive to the change of crisis windows, as the model
consistently performed well in predicting the in-sample currency crises. In
addition, signal 2, when predicting the currency crisis within a 12-month crisis
window, performed better than the benchmark model. In contrast, for the out-
of-sample prediction, this study found that the signal model was more sensitive
and less consistent, for the models with shorter crisis windows performed less
well when compared to the benchmark model.
Finally, to conclude this subsection, it was found that the application of the
signal model using a 24-month crisis window was more consistent when
predicting the Indonesian currency crises, for it could predict the out-of-sample
crisis better than the model using shorter crisis windows. This finding also
supports previous studies such as Kaminsky et al. (1998), Edison (2000),
Goldstein et al. (2000), and Kaminsky (1999), all of which indicate a 24-month
crisis window is more consistent when predicting currency crises. Moreover, in
comparing the performance between these two signal model options, this study
found the signal model based on option 2 generally performed better than the
model based on option 1 across crisis windows and cut-off probabilities.
7.3.2. The Probit Model
For consistency and comparability, and similar to the previous subsection,
sensitivity tests are here carried out so as to observe the consistency of the
probit model by predicting the Indonesian currency crises within shorter crisis
windows, such as 6, 12, and 18 months. After adjusting the dependent variable
for these three shorter crisis windows, as in Chapter 5, the probit model applies
180
Huber/White robust errors and covariance to predict the currency crises in
Indonesia within the 24-month crisis window. This is later referred to as the
benchmark model. Towards this purpose, these new crises dependent variables
are regressed using the same 10 explanatory variables with the benchmark
model. The regression results for these three shorter crisis windows are shown
in Table 7.8.
TABLE 7.8 The Probit Model’s Regression Results for Various Crisis Windows
Variables Exp. sign
Regression Coefficient
6m 12m 18m 24m
Constant -5.686*
(-2.812) -5.465*
(-2.880) -7.830*
(-4.005) -3.806**
(-2.175)
Real US$/yen exchange ratea - 0.047*
(3.176) 0.053*
(4.137) 0.032*** (1.799)
-0.002 (-0.175)
Short-term capital flows to GDP
- -63.660* (-3.180)
-81.496* (-3.916)
-85.959* (-3.576)
-91.104* (-3.964)
US annual growth rate - 0.014
(0.180) 0.114
(1.516) 0.137***
(1.798) 0.097
(1.175)
US real interest ratec + -0.243** (-2.133)
-0.411* (-3.656)
-0.510* (-3.027)
-0.061 (-1.264)
US real interest rate + 0.213** (2.415)
0.226** (2.191)
0.185* (3.313)
0.065 (1.231)
Loans to depositsc + -2.157** (-2.043)
-2.430** (-2.112)
-0.731 (-1.320)
1.405* (3.238)
M1 to GDPc + -60.915* (-3.321)
-7.345 (-0.378)
35.000** (1.929)
31.081*** (1.828)
Real effective exchange ratea + 0.018** (2.334)
0.036* (4.737)
0.049* (4.983)
0.049* (4.323)
Exportsb - -0.028*
(-2.963) -0.022*
(-3.351) -0.020*
(-2.962) -0.017*
(-2.962)
M1 to GDP + 31.736 (1.541)
29.517 (1.495)
58.253* (2.799)
28.212 (1.495)
McFadden R2 0.401 0.493 0.584 0.597
Number of observation 300 300 300 300 Note: the z-statistics are shown in parentheses; One star (*) indicates statistical significance at a 1% level, Two stars (**) indicates significance at a 5% level. Three stars (***) indicates significance at a 10% level; Alphabet (a) indicates 12 m change; Alphabet (b) indicates 12 m % change, Alphabet (c) indicates deviation from trend-HP Filter
As a consequence of using these shorter crisis windows, Table 7.8 shows that
similar to the benchmark model, these new probit models also have some
explanatory variables with statistically insignificant coefficients and wrong
signs, and the number of variables varied across crisis windows. For examples,
probit 1 has two insignificant variables, while probit 2 and 3 have three, and
one insignificant variable, respectively. Unlike the benchmark model that has
181
only two independent variables with the wrong sign, probit 1 and 2 have five
variables with wrong signs, while probit 3 has four such variables.
Moreover, to find out the contribution of each explanatory variable in
determining the currency crises in Indonesia, this study also calculates the
marginal effect for each explanatory variable for all models relative to the
currency crisis dependent variables. These results are presented in Table 7.9. As
with the benchmark model, this study found that all models indicated the
short-term capital flows to GDP as the most significant contributors in
determining the Indonesian currency crises for all crisis windows.
TABLE 7.9 Determinants of Indonesian Currency Crises
Variables dProb/dx
6m 12m 18m 24m
Real US$/yen exchange ratea 0.0002 0.0009 0.0011 -0.0002
Short-term capital flows to GDP -0.3088 -1.4183 -3.0354 -10.2901
US annual growth rate 0.0001 0.0020 0.0048 0.0109
US real interest ratec -0.0012 -0.0072 -0.0180 -0.0068
US real interest rate 0.0010 0.0039 0.0065 0.0073
Loans to depositsc -0.0105 -0.0423 -0.0258 0.1587
M1 to GDPc -0.2955 -0.1278 1.2359 3.5106
Real effective exchange ratea 0.0001 0.0006 0.0017 0.0055
Exportsb -0.0001 -0.0004 -0.0007 -0.0020
M1 to GDP 0.1539 0.5137 2.0571 3.1866
The probability of a crisis for probit models with shorter crisis windows is
presented in Figures 7.7 and 7.8. This shows (a) the probit model for predicting
a 6-month crisis window (probit 1), (b) the probit model for predicting a 12-
month crisis window (probit 2), and (c) the probit model for predicting a 18-
month crisis window (probit 3). In addition, the performance assessments for
these models are presented in Tables 7.10 for in-sample evaluation, and 7.11 for
out-of-sample evaluation.
182
The Probit Model: In-Sample Prediction
As indicated in Figure 7.7, the ability of probit models to capture all three in-
sample currency crises, namely November 1978, April 1983 and September
1986, tended to increase in line with the increase in the prediction horizon or
crisis window. Compared to the other two models, probit 1 has the lowest
predictive ability, as it cannot predict the third in-sample crisis. This is also
supported by the ratio of pre-crisis period accurately predicted in Table 7.10.
For example, at Pr*=20%, these models predict the in-sample currency crises
quite well, as their prediction reached around 61% (probit 1), 81% (probit 2) and
87% (probit 3). Furthermore their predictive power drops following a further
increase in Pr*. For example, at Pr*=50%, probit 1 is only able to capture 28% of
pre-crisis periods, while the other two models still perform well as they predict
58% (probit 2) and 65% (probit 3). However, during this period, the benchmark
model performed better than these models, as it was able to predict these pre-
crises periods by 75% (Pr*=50%) to 94% (Pr*=20%). Moreover, Table 7.10 also
shows that the in-sample predictive ability of the probit model tends to increase
as the prediction horizon is extended.
This figure also indicates that these models send false alarms during which
their probability of a crisis crosses their thresholds beyond their pre-crisis
period, as indicated by the yellow shaded areas. Regarding the number of false
alarms relative to the total signals, Table 7.10 confirms that these models send
more false alarms compared to the benchmark model. For example, the number
of in-sample false alarms relative to total signals was around 60% (probit 1),
51% (probit 2) and 41% (probit 3) at Pr*=20%, decreasing to around 20% as Pr*
increased to 50%.
Regarding the ability to predict the entire observation, including pre-crisis and
tranquil periods, these models show positive performance, as they are able to
predict more than 87% at Pr*=20%, and show a further increase to around 96%
when Pr*=50%.
FIGURE 7.7
0%
20%
40%
60%
80%
100%
19
71
M0
1
19
71
M1
2
19
72
M1
1
19
73
M1
0
19
74
M0
9
0%
20%
40%
60%
80%
100%
19
71
M0
1
19
71
M1
2
19
72
M1
1
19
73
M1
0
19
74
M0
9
0%
20%
40%
60%
80%
100%
19
71
M0
1
19
71
M1
2
19
72
M1
1
19
73
M1
0
19
74
M0
9
183
FIGURE 7.7 The Probit Model’s In-Sample Prediction
(a) A 6-month crisis window (probit 1)
(b) A 12-month crisis window (probit 2)
(c) A 18-month crisis window (probit 3)
19
74
M0
9
19
75
M0
8
19
76
M0
7
19
77
M0
6
19
78
M0
5
19
79
M0
4
19
80
M0
3
19
81
M0
2
19
82
M0
1
19
82
M1
2
19
83
M1
1
19
84
M1
0
19
85
M0
9
19
86
M0
8
19
87
M0
7
19
88
M0
6
19
89
M0
5
19
90
M0
4
cc6m probit_6m
19
74
M0
9
19
75
M0
8
19
76
M0
7
19
77
M0
6
19
78
M0
5
19
79
M0
4
19
80
M0
3
19
81
M0
2
19
82
M0
1
19
82
M1
2
19
83
M1
1
19
84
M1
0
19
85
M0
9
19
86
M0
8
19
87
M0
7
19
88
M0
6
19
89
M0
5
19
90
M0
4
cc12m probit_12m
19
74
M0
9
19
75
M0
8
19
76
M0
7
19
77
M0
6
19
78
M0
5
19
79
M0
4
19
80
M0
3
19
81
M0
2
19
82
M0
1
19
82
M1
2
19
83
M1
1
19
84
M1
0
19
85
M0
9
19
86
M0
8
19
87
M0
7
19
88
M0
6
19
89
M0
5
19
90
M0
4
cc18m probit_18m
ample Prediction
(probit 1)
19
90
M0
4
19
91
M0
3
19
92
M0
2
19
93
M0
1
19
93
M1
2
19
94
M1
1
19
95
M1
0
19
90
M0
4
19
91
M0
3
19
92
M0
2
19
93
M0
1
19
93
M1
2
19
94
M1
1
19
95
M1
0
19
90
M0
4
19
91
M0
3
19
92
M0
2
19
93
M0
1
19
93
M1
2
19
94
M1
1
19
95
M1
0
184
These results are supported by high performance in capturing the tranquil
periods and the tranquil period is much longer than the crisis periods.
Similarly, these models also have high accuracy and calibration as indicated by
lower QPS and GSB scores. Furthermore, their results are slightly better than
the benchmark model.
TABLE 7.10 The Probit Model’s In-Sample Evaluation
Pr* Assessment methods In-sample (1971-1995)
6m 12m 18m 24m
20%
% of observations correctly called 92.33% 87.67% 87.00% 88.67%
% of pre-crisis periods correctly called 61.11% 80.56% 87.04% 94.44%
% of tranquil periods correctly called 94.33% 88.64% 86.99% 86.84%
% of false alarms of total alarms 59.26% 50.85% 40.51% 30.61%
QPS 0.1533 0.2467 0.2600 0.2267
GSB 0.0018 0.0118 0.0139 0.0150
30%
% of observations correctly called 93.67% 89.67% 90.67% 90.00%
% of pre-crisis periods correctly called 44.44% 63.89% 85.19% 88.89%
% of tranquil periods correctly called 96.81% 93.18% 91.87% 90.35%
% of false alarms of total alarms 52.94% 43.90% 30.30% 25.58%
QPS 0.1267 0.2067 0.1867 0.2000
GSB 0.0000 0.0006 0.0032 0.0044
40%
% of observations correctly called 95.00% 91.67% 90.67% 91.33%
% of pre-crisis periods correctly called 38.89% 58.33% 72.22% 87.50%
% of tranquil periods correctly called 98.58% 96.21% 94.72% 92.54%
% of false alarms of total alarms 36.36% 32.26% 25.00% 21.25%
QPS 0.1000 0.1667 0.1867 0.1733
GSB 0.0011 0.0006 0.0001 0.0014
50%
% of observations correctly called 95.00% 93.00% 90.67% 89.33%
% of pre-crisis periods correctly called 27.78% 58.33% 64.81% 75.00%
% of tranquil periods correctly called 99.29% 97.73% 96.34% 93.86%
% of false alarms of total alarms 28.57% 22.22% 20.45% 20.59%
QPS 0.1000 0.1400 0.1867 0.2133
GSB 0.0027 0.0018 0.0022 0.0004 Note: Pr*: the cut-off probability
The Probit Model: Out-of-Sample Prediction
To test the ability of these models in predicting crisis, this study predicts the
out-of-sample currency crisis from 1996 to 2008. Based on Figure 7.8, these
models are able to predict the presence of the Asian Financial Crisis, as their
probability increases over their cut-off probabilities during this pre-crisis
period, which is indicated by the yellow shaded area. It is also seen in Table
7.11 where these three models are able to predict this crisis by 38% (probit 1),
185
59% (probit 2) and 57% (probit 3) at Pr*=20%. Except probit 1, the performances
of other probit models are better than the benchmark model. However as Pr*
increases to 50%, the prediction of probit 1 remains the same, although the
performance of the other models, including the benchmark model, decreases,
but probit 3 is still able to predict the crisis by 46%, which is better than the
benchmark model that is only able to predict by 43%.
In terms of the timing of warning signals, and based on figure 7.8, probits 1 and
2 sent warning signals much later than probit 3. Probit 1 was expected to send
warning signals from March 1997 but it only started to send significant
warnings from January 1998 when the probability of a crisis reached 100%.
Similar to the benchmark model, probit 2 also sent warning signals from
January 1997 with the probability of a crisis being 34%. This was four months
after its pre-crisis period, which started in September 1996. Unlike these two
models, probit 3 was able to transmit its warning signals from the beginning of
its pre-crisis period, although its probability of a crisis was not so significant,
being only 18%. This increased to 34% in June 1996, but then continued to fall,
to then jump to 75% after January 1997.
As with the benchmark model, Figure 7.8 also shows lots of false alarms with
signals failing to be followed by any crisis within their crisis windows.
Furthermore, the number of false alarms generated in this period exceeded
those in the in-sample period. Based on Table 7.9, these three models sent out
many false alarms, showing that for the entire out-of-sample, for example at
Pr*=20%, the average false alarms relative to all signals was about 80%. Even
when Pr* was raised to 50%, the ratio of false alarms remained high.
Although the ability of these models in capturing the tranquil periods is still
better than the benchmark model, these high false signals impede the ability of
the models in capturing the information. It also eventually affects the ability of
these models to capture the entire observations, for both periods of crisis and of
tranquility. For the entire samples, they can predict about 38% (probit 1) of all
observations, 59% for probit 2 and 57% for probit 3, at Pr*=20%.
FIGURE 7.8 The P
(a) A 6-month crisis window
(b) A 12-month
(c) A 18-month crisis window (prob
0%
20%
40%
60%
80%
100%1
99
6M
01
19
96
M0
7
19
97
M0
1
19
97
M0
7
19
98
M0
1
19
98
M0
7
19
99
M0
1
19
99
M0
7
20
00
M0
1
0%
20%
40%
60%
80%
100%
19
96
M0
1
19
96
M0
7
19
97
M0
1
19
97
M0
7
19
98
M0
1
19
98
M0
7
19
99
M0
1
19
99
M0
7
20
00
M0
1
0%
20%
40%
60%
80%
100%
19
96
M0
1
19
96
M0
7
19
97
M0
1
19
97
M0
7
19
98
M0
1
19
98
M0
7
19
99
M0
1
19
99
M0
7
20
00
M0
1
186
The Probit Model’s Out-of-Sample Prediction
month crisis window (probit 1)
month crisis window (probit 2)
month crisis window (probit 3)
20
00
M0
1
20
00
M0
7
20
01
M0
1
20
01
M0
7
20
02
M0
1
20
02
M0
7
20
03
M0
1
20
03
M0
7
20
04
M0
1
20
04
M0
7
20
05
M0
1
20
05
M0
7
20
06
M0
1
20
06
M0
7
20
07
M0
1
20
07
M0
7
20
08
M0
1
cc6m probit_6m
20
00
M0
1
20
00
M0
7
20
01
M0
1
20
01
M0
7
20
02
M0
1
20
02
M0
7
20
03
M0
1
20
03
M0
7
20
04
M0
1
20
04
M0
7
20
05
M0
1
20
05
M0
7
20
06
M0
1
20
06
M0
7
20
07
M0
1
20
07
M0
7
20
08
M0
1
cc12m probit_12m
20
00
M0
1
20
00
M0
7
20
01
M0
1
20
01
M0
7
20
02
M0
1
20
02
M0
7
20
03
M0
1
20
03
M0
7
20
04
M0
1
20
04
M0
7
20
05
M0
1
20
05
M0
7
20
06
M0
1
20
06
M0
7
20
07
M0
1
20
07
M0
7
20
08
M0
1
cc18m probit_18m
20
08
M0
1
20
08
M0
7
20
08
M0
1
20
08
M0
7
20
08
M0
1
20
08
M0
7
187
However, when Pr* increases, their prediction for all observations only slightly
increases, although this is still better than the benchmark model. Similar results
have also occurred when related to the level of accuracy and calibration of these
models, which are based on the QPS and GSB scores. This indicates that these
models are more useful than the benchmark models, as seen in Table 7.11.
The above discussion shows that in comparing the predicted results within
these various alternative crisis windows, this model was less effective when
predicting currency crises within the 6-months crisis window. Although probit
1 was capable of predicting the out-of-sample crisis by 38%, its generated
signals proved too late for January 1998. But the ability of this model tends to
increase when used for longer prediction horizons or crisis windows, such as 12
and 18 months, for which the model was consistently able to predict the
currency crises both in-sample and out-of-sample, as well as to show improved
accuracy in the timing of signals transmitted. Furthermore, Figure 7.8 and Table
7.11 also show that the performance of probit 3 was slightly better than the
benchmark model in predicting the Asian Financial Crisis in 1997/98, as it was
in the timing of warning signals sent.
7.3.3. The Artificial Neural Network Model
As with the two previous models, namely the signal and probit models, this
study also conducts a sensitivity test to see the consistency of the artificial
neural network (ANN) model. This was applied in Chapter 6 to predict the
Indonesian currency crises when the crisis windows shortened to 6 (ANN 1), 12
(ANN 2), and 18 (ANN 3) months. For this purpose, and similar to the
benchmark ANN model, this study applies the multilayer feed-forward neural
network with three layers, which consists of ten input neurons, one hidden
layer with ten hidden neurons and one output neuron. In addition, to increase
the performance of these new ANN models, they were trained by employing
the back-propagation supervised learning algorithm using the in-sample data
set from January 1971 to December 1995. These models were then simulated
using the same training parameters to the benchmark model, as presented in
Table 7.12.
18
8
TA
BL
E 7
.11
The
Pro
bit
Mod
el’s
Ou
t-of
-sam
ple
Eva
luat
ion
Pr*
Assessment methods
Out-of-sample Prediction
1996-1998
1996-2008
6m
12m
18m
24m
6m
12m
18m
24m
20%
% o
f o
bse
rvat
ion
s co
rrec
tly
cal
led
55
.56%
58
.33%
50
.00%
44
.44%
74
.51%
65
.36%
52
.94%
48
.37%
% o
f p
re-c
risi
s p
erio
ds
corr
ectl
y c
alle
d
37.5
0%
59.0
9%
57.1
4%
53.3
3%
37.5
0%
59.0
9%
57.1
4%
53.3
3%
% o
f tr
anq
uil
per
iod
s co
rrec
tly
cal
led
70
.00%
57
.14%
25
.00%
0.
00%
78
.83%
66
.41%
52
.00%
47
.15%
% o
f fa
lse
alar
ms
of
tota
l al
arm
s 50
.00%
31
.58%
27
.27%
27
.27%
82
.86%
77
.19%
78
.95%
80
.25%
QP
S
0.88
89
0.83
33
1.00
00
1.11
11
0.50
98
0.69
28
0.94
12
1.03
27
GS
B
0.02
47
0.01
39
0.05
56
0.09
88
0.03
08
0.10
47
0.19
69
0.22
22
30%
% o
f o
bse
rvat
ion
s co
rrec
tly
cal
led
55
.56%
52
.78%
44
.44%
38
.89%
75
.82%
67
.32%
56
.86%
52
.94%
% o
f p
re-c
risi
s p
erio
ds
corr
ectl
y c
alle
d
37.5
0%
50.0
0%
50.0
0%
46.6
7%
37.5
0%
50.0
0%
50.0
0%
46.6
7%
% o
f tr
anq
uil
per
iod
s co
rrec
tly
cal
led
70
.00%
57
.14%
25
.00%
0.
00%
80
.29%
70
.23%
58
.40%
54
.47%
% o
f fa
lse
alar
ms
of
tota
l al
arm
s 50
.00%
35
.29%
30
.00%
30
.00%
81
.82%
78
.00%
78
.79%
80
.00%
QP
S
0.88
89
0.94
44
1.11
11
1.22
22
0.48
37
0.65
36
0.86
28
0.94
12
GS
B
0.02
47
0.03
86
0.09
88
0.15
43
0.02
47
0.06
70
0.12
34
0.13
67
40
%
% o
f o
bse
rvat
ion
s co
rrec
tly
cal
led
55
.56%
41
.67%
41
.67%
38
.89%
75
.82%
69
.28%
60
.13%
56
.21%
% o
f p
re-c
risi
s p
erio
ds
corr
ectl
y c
alle
d
37.5
0%
31.8
2%
46.4
3%
46.6
7%
37.5
0%
31.8
2%
46.4
3%
46.6
7%
% o
f tr
anq
uil
per
iod
s co
rrec
tly
cal
led
70
.00%
57
.14%
25
.00%
0.
00%
80
.29%
75
.57%
63
.20%
58
.54%
% o
f fa
lse
alar
ms
of
tota
l al
arm
s 50
.00%
46
.15%
31
.58%
30
.00%
81
.82%
82
.05%
77
.97%
78
.46%
QP
S
0.88
89
1.16
67
1.16
67
1.22
22
0.48
37
0.61
44
0.79
74
0.87
58
GS
B
0.02
47
0.12
50
0.12
50
0.15
43
0.02
47
0.02
47
0.08
21
0.10
47
50%
% o
f o
bse
rvat
ion
s co
rrec
tly
cal
led
55
.56%
38
.89%
41
.67%
36
.11%
75
.82%
69
.28%
64
.71%
60
.78%
% o
f p
re-c
risi
s p
erio
ds
corr
ectl
y c
alle
d
37.5
0%
27.2
7%
46.4
3%
43.3
3%
37.5
0%
27.2
7%
46.4
3%
43.3
3%
% o
f tr
anq
uil
per
iod
s co
rrec
tly
cal
led
70
.00%
57
.14%
25
.00%
0.
00%
80
.29%
76
.34%
68
.80%
65
.04%
% o
f fa
lse
alar
ms
of
tota
l al
arm
s 50
.00%
50
.00%
31
.58%
31
.58%
81
.82%
83
.78%
75
.00%
76
.79%
QP
S
0.88
89
1.22
22
1.16
67
1.27
78
0.48
37
0.61
44
0.70
59
0.78
43
GS
B
0.02
47
0.15
43
0.12
50
0.18
67
0.02
47
0.01
92
0.04
921
0.05
78
189
As in Chapter 6, and similar to the benchmark model, this model initially set its
connection weights randomly, and then during the training process, the
connection weights were adjusted gradually until the model’s output came
close to its target value, or the error becomes smaller, or the maximum number
of iterations was reached. The maximum number of iterations was set at 30000.
Based on these training parameters, after the model reached the maximum
number of iterations, it was found that the training error for these new ANN
models was much larger than the benchmark model (0.062), and varied across
crisis windows. For example, 0.5007 (ANN 1), 0.3847 (ANN 2) and 0.7203 (ANN
3).
TABLE 7.12 The Training Parameter for ANN Models
No Description Training Information
1 Type of network Multi-layer perceptron
2 Number of layers 3
3 Number of hidden layer 1
4 Number of input neurons 10
5 Number of hidden neurons 10
6 Number of output neurons 1
7 Activation functions Logistic
8 Performance function Mean squared error
9 Training algorithm back-propagation
10 Starting weights and biases Random
11 Number of iterations 30000
12 Training error 0.062
13 Learning rate (α) 0.010
14 Momentum factor (β) 0.800
Similar to the parametric approach using the probit model, and based on the
results of the training undertaken, this model can be used to show the average
contribution for each input’s neurons to the output neuron, that is, the
probability of the occurrence of currency crises in Indonesia. However, unlike
the parametric model, it should be noted that in the ANN model, beyond the
190
input neurons there are still many other factors that may affect or determine the
output of this model, such as the number of hidden layers and hidden neurons,
the value of momentum and learning rates, and the number of iteration.
Table 7.13 presents the average contribution of input neurons to the output
neuron for all crisis windows. Except for ANN 2 that refers to the US real
interest rate as the main contributor to its output, the other models, namely
ANN 1 and 3, as well as the benchmark model, point out that the real effective
exchange rate is the main contributor that determines the probability of the
occurrence of currency crises in Indonesia, the average contribution being 16-
17%. The table also shows that they vary across the prediction horizons for
other contributors.
The in-sample forecasting results of these models are presented in Figure 7.9,
while Figure 7.10 displays the out-of-sample forecasting results. Furthermore,
the in-sample performance assessment results are recorded in Table 7.14, while
Table 7.15 presents the out-of-sample assessment results.
TABLE 7.13 Average Contribution of Input Nodes to Output Node
No Description Average Contribution
6m 12m 18m 24m
1 Short-term capital flows to GDP 5.61% 6.90% 6.69% 8.64%
2 Exportsb 6.08% 5.19% 13.80% 8.59%
3 Real effective exchange ratea 17.02% 8.79% 15.87% 17.40%
4 M1 to GDP 8.08% 6.06% 5.20% 6.93%
5 M1 to GDPc 10.72% 10.25% 5.15% 7.28%
6 Loans to depositsc 9.55% 12.11% 9.30% 13.11%
7 US real interest rate 14.10% 16.57% 15.27% 10.55%
8 US real interest ratec 9.25% 11.06% 12.38% 10.84%
9 US annual growth rate 9.76% 14.95% 7.60% 6.94%
10 Real US$/yen exchange ratea 9.81% 8.12% 8.73% 9.72% Notes: a deviation from trend-HP filter, b 12 months percentage change, c 12 months change
191
The ANN Model: In-sample Prediction
Figure 7.9 shows that similar to the benchmark model, these models can also
predict all in-sample currency crises accurately, as their probability of a crisis
rises throughout the entire pre-crisis periods, these being marked as yellow
areas. It is also supported by the percentage of the pre-crises period correctly
called in Table 7.14 that indicates all models are able to predict 100% of pre-
crises periods for all cut-off probabilities.
In addition, as with the benchmark model, this figure shows these models send
fewer false alarms, thus increasing the accuracy of these models in capturing
the tranquil periods. It also increases the ability of these models to capture the
whole observation for both crisis and tranquil periods. Furthermore, this table
records that their QPS and GSB scores are almost close to zero, which indicates
that the accuracy and calibration of these models are almost perfect.
FIGURE 7.9 The A
(a) A 6-month crisis window (ANN 1)
(b) A 12-month crisis window (ANN 2)
(c) A 18-month crisis windo
0%
20%
40%
60%
80%
100%1
97
1M
01
19
71
M1
2
19
72
M1
1
19
73
M1
0
19
74
M0
9
19
75
M0
8
19
76
M0
7
19
77
M0
6
19
78
M0
5
0%
20%
40%
60%
80%
100%
19
71
M0
1
19
71
M1
2
19
72
M1
1
19
73
M1
0
19
74
M0
9
19
75
M0
8
19
76
M0
7
19
77
M0
6
19
78
M0
5
0%
20%
40%
60%
80%
100%
19
71
M0
1
19
71
M1
2
19
72
M1
1
19
73
M1
0
19
74
M0
9
19
75
M0
8
19
76
M0
7
19
77
M0
6
19
78
M0
5
192
The ANN Model’s In-Sample Prediction
month crisis window (ANN 1)
month crisis window (ANN 2)
month crisis window (ANN 3)
19
79
M0
4
19
80
M0
3
19
81
M0
2
19
82
M0
1
19
82
M1
2
19
83
M1
1
19
84
M1
0
19
85
M0
9
19
86
M0
8
19
87
M0
7
19
88
M0
6
19
89
M0
5
19
90
M0
4
19
91
M0
3
19
92
M0
2
19
93
M0
1
19
93
M1
2
cc6m ann_6m
19
79
M0
4
19
80
M0
3
19
81
M0
2
19
82
M0
1
19
82
M1
2
19
83
M1
1
19
84
M1
0
19
85
M0
9
19
86
M0
8
19
87
M0
7
19
88
M0
6
19
89
M0
5
19
90
M0
4
19
91
M0
3
19
92
M0
2
19
93
M0
1
19
93
M1
2
cc12m ann_12m
19
79
M0
4
19
80
M0
3
19
81
M0
2
19
82
M0
1
19
82
M1
2
19
83
M1
1
19
84
M1
0
19
85
M0
9
19
86
M0
8
19
87
M0
7
19
88
M0
6
19
89
M0
5
19
90
M0
4
19
91
M0
3
19
92
M0
2
19
93
M0
1
19
93
M1
2
cc18m ann_18m
19
93
M1
2
19
94
M1
1
19
95
M1
0
19
93
M1
2
19
94
M1
1
19
95
M1
0
19
93
M1
2
19
94
M1
1
19
95
M1
0
193
TABLE 7.14 The ANN Model’s In-Sample Evaluation
Pr* Assessment methods In-sample (1971-1995)
6m 12m 18m 24m
20%
% of observations correctly called 98.67% 99.33% 98.00% 98.33%
% of pre-crisis periods correctly called 100.00% 100.00% 100.00% 100.00%
% of tranquil periods correctly called 98.58% 99.24% 97.56% 97.81%
% of false alarms of total alarms 18.18% 5.26% 10.00% 6.49%
QPS 0.0267 0.0133 0.0400 0.0333
GSB 0.0004 0.0001 0.0008 0.0006
30%
% of observations correctly called 99.33% 100.00% 99.33% 99.67%
% of pre-crisis periods correctly called 100.00% 100.00% 98.15% 100.00%
% of tranquil periods correctly called 99.29% 100.00% 99.59% 99.56%
% of false alarms of total alarms 10.00% 0.00% 1.85% 1.37%
QPS 0.0133 0.0000 0.0133 0.0067
GSB 0.0001 0.0000 0.0000 0.0000
40%
% of observations correctly called 99.67% 100.00% 99.67% 99.67%
% of pre-crisis periods correctly called 100.00% 100.00% 98.15% 98.61%
% of tranquil periods correctly called 99.65% 100.00% 100.00% 100.00%
% of false alarms of total alarms 5.26% 0.00% 0.00% 0.00%
QPS 0.0067 0.0000 0.0067 0.0067
GSB 0.0000 0.0000 0.0000 0.0000
50%
% of observations correctly called 99.67% 100.00% 99.67% 99.67%
% of pre-crisis periods correctly called 100.00% 100.00% 98.15% 98.61%
% of tranquil periods correctly called 99.65% 100.00% 100.00% 100.00%
% of false alarms of total alarms 5.26% 0.00% 0.00% 0.00%
QPS 0.0067 0.0000 0.0067 0.0067
GSB 0.0000 0.0000 0.0000 0.0000
Note: Pr*: the cut-off probability
The ANN Model: Out-of-sample Prediction
In this section, an attempt was made to test the ability of these models to predict
the Asian Financial Crisis that occurred in Indonesia during the period 1997/98.
The prediction of these models can be seen in Figure 7.10, and the results of the
assessment of the performance in predicting the out-of-sample are presented in
Table 7.15.
FIGURE 7.10 The A
(a) A 6-month
(b) A 12-month crisis window (ANN 2
(c) A 18-month crisis window (ANN 3)
0%
20%
40%
60%
80%
100%1
99
6M
01
19
96
M0
7
19
97
M0
1
19
97
M0
7
19
98
M0
1
19
98
M0
7
19
99
M0
1
19
99
M0
7
20
00
M0
1
0%
20%
40%
60%
80%
100%
19
96
M0
1
19
96
M0
7
19
97
M0
1
19
97
M0
7
19
98
M0
1
19
98
M0
7
19
99
M0
1
19
99
M0
7
20
00
M0
1
0%
20%
40%
60%
80%
100%
19
96
M0
1
19
96
M0
7
19
97
M0
1
19
97
M0
7
19
98
M0
1
19
98
M0
7
19
99
M0
1
19
99
M0
7
20
00
M0
1
194
The ANN Model’s Out-of-Sample Prediction
month crisis window (ANN 1)
month crisis window (ANN 2)
month crisis window (ANN 3)
20
00
M0
1
20
00
M0
7
20
01
M0
1
20
01
M0
7
20
02
M0
1
20
02
M0
7
20
03
M0
1
20
03
M0
7
20
04
M0
1
20
04
M0
7
20
05
M0
1
20
05
M0
7
20
06
M0
1
20
06
M0
7
20
07
M0
1
20
07
M0
7
20
08
M0
1
cc6m ann_6m
20
00
M0
1
20
00
M0
7
20
01
M0
1
20
01
M0
7
20
02
M0
1
20
02
M0
7
20
03
M0
1
20
03
M0
7
20
04
M0
1
20
04
M0
7
20
05
M0
1
20
05
M0
7
20
06
M0
1
20
06
M0
7
20
07
M0
1
20
07
M0
7
20
08
M0
1
cc12m ann_12m
20
00
M0
1
20
00
M0
7
20
01
M0
1
20
01
M0
7
20
02
M0
1
20
02
M0
7
20
03
M0
1
20
03
M0
7
20
04
M0
1
20
04
M0
7
20
05
M0
1
20
05
M0
7
20
06
M0
1
20
06
M0
7
20
07
M0
1
20
07
M0
7
20
08
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cc18m ann_18m
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7
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195
Based on this figure, it is found that these models can predict the existence of
this crisis where the crisis probability of these three models is developed to
predict crisis for each crisis window, which tends to rise prior to the crisis. Even
though the predictions are not as good as the benchmark ANN model, Table
7.11 shows that these models are capable of predicting the pre-crisis period by
about 73%, except for ANN 1 which can only predict about 56% at Pr*=20%. As
with other previous models, the ability tended to fall as the cut-off probability
increases. At Pr*=50%, the prediction ability of ANN 3 fell by 14% from 75% to
61%, while the other two models only decreased slightly to 50% (ANN 1) and
68% (ANN 2).
With regard to the timing of the signals transmitted, this figure shows that these
three models were capable of sending warning signals from early 1996, or a few
months before their prediction horizon. This was unlike the benchmark model
which used the 24-crisis window, but because of using shorter crisis windows,
these signals can be classified as false alarms because no currency crises
occurred within the crisis windows (Goldstein et al., 2000). However, based on
this figure, ANN 1 sent warning signals from June 1997 with the probability of
a crisis of 64%, and even after that it dropped to 0% but rose again to 100% in
January 1998. Meanwhile for ANN 2, although a bit late, this model was
capable of sending warning signals from January 1997, with the probability of a
crisis reaching 100%, but dropping to 12% in November 1997 before rising again
to 100% in January 1998.
Unlike the other two ANN models with shorter crisis windows, ANN 3 was
able to transmit warning signals from the beginning of the prediction horizon
with its probability of a crisis reaching 79%, despite the probability of a crisis
having come down in October 1996. This rose again to 68% in January 1997, but
after September 1997 it dropped dramatically before rising again to 100% in
January 1998. Furthermore, based on Figure 7.9, this study found that as its
prediction horizon expanded the model tended to transmit its warning signals
earlier.
196
Apart from the ability to predict crises, Figure 7.10 also shows that after the
Asian Financial Crisis in 1997/98, and until the end of 2008, these models still
sent many warning signals of impending crisis in Indonesia. However, as
previously stated, based on Equation 3.2 in Chapter 3, no currency crises were
found in Indonesia during this period and these signals can be classified as false
alarms. According to Table 7.15, during this period, compared to the
benchmark model, these models sent more false alarms by 70% at Pr*=20%,
dropping slightly as Pr* increased to 50%. Furthermore these high false alarms
also lowered the ability of the model in capturing the tranquil periods. This
table also indicates that the ability of the models in capturing the tranquil
period tended to decline with the increase in the prediction horizons. It also
applies for the ability of the models to capture the whole observation, as well as
for the level of accuracy and calibration for these models.
To conclude, the purpose of this section has been to exercise the consistency of
the ANN model by applying the sensitivity test to assess its performance in
predicting currency crises in Indonesia for difference crisis windows. Based on
the discussion above, it is found that the ANN model consistently performed
very well in predicting the in-sample currency crises from January 1971 to
December 1995 for all crisis windows. With fewer false alarms it acted perfectly
in capturing tranquil periods and ultimately enhanced the level of accuracy and
calibration of this model for all crisis windows. For the out-of-sample
predictions from 1996 to 2008, although the prediction outcomes are not as
good compared to the in-sample prediction and benchmark models, this model
was still able to predict the Asian Financial Crisis by more than 50% to 75% of
its pre-crisis period.
19
7
TA
BL
E 7
.15
The
AN
N M
odel
’s O
ut-
of-s
ampl
e E
valu
atio
n
Pr*
Assessm
ent methods
Out-of-sample Prediction
1996-1998
1996-2008
6m
12m
18m
24m
6m
12m
18m
24m
20%
% o
f o
bse
rvat
ion
s co
rrec
tly
cal
led
47
.22%
50
.00%
58
.33%
88
.89%
73
.86%
69
.93%
58
.82%
69
.28%
% o
f p
re-c
risi
s p
erio
ds
corr
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y c
alle
d
56.2
5%
72.7
3%
75.0
0%
96.6
7%
56.2
5%
72.7
3%
75.0
0%
96.6
7%
% o
f tr
anq
uil
per
iod
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tly
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led
40
.00%
14
.29%
0.
00%
50
.00%
75
.91%
69
.47%
55
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62
.60%
% o
f fa
lse
alar
ms
of
tota
l al
arm
s 57
.14%
42
.86%
27
.59%
9.
38%
78
.57%
71
.43%
72
.73%
61
.33%
QP
S
1.05
56
1.00
00
0.83
33
0.22
22
0.52
29
0.60
13
0.82
35
0.61
44
GS
B
0.03
86
0.05
56
0.00
15
0.00
62
0.05
78
0.09
88
0.20
51
0.17
30
30%
% o
f o
bse
rvat
ion
s co
rrec
tly
cal
led
47
.22%
50
.00%
47
.22%
86
.11%
75
.16%
72
.55%
60
.13%
71
.90%
% o
f p
re-c
risi
s p
erio
ds
corr
ectl
y c
alle
d
56.2
5%
72.7
3%
60.7
1%
93.3
3%
56.2
5%
72.7
3%
60.7
1%
93.3
3%
% o
f tr
anq
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per
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s co
rrec
tly
cal
led
40
.00%
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.29%
0.
00%
50
.00%
77
.37%
72
.52%
60
.00%
66
.67%
% o
f fa
lse
alar
ms
of
tota
l al
arm
s 57
.14%
42
.86%
32
.00%
9.
68%
77
.50%
69
.23%
74
.63%
59
.42%
QP
S
1.05
56
1.00
00
1.05
56
0.27
78
0.49
67
0.54
90
0.79
74
0.56
21
GS
B
0.03
86
0.05
56
0.01
39
0.00
15
0.04
92
0.07
69
0.13
00
0.13
00
40
%
% o
f o
bse
rvat
ion
s co
rrec
tly
cal
led
44
.44%
50
.00%
47
.22%
86
.11%
74
.51%
73
.20%
62
.09%
74
.51%
% o
f p
re-c
risi
s p
erio
ds
corr
ectl
y c
alle
d
50.0
0%
68.1
8%
60.7
1%
93.3
3%
50.0
0%
68.1
8%
60.7
1%
93.3
3%
% o
f tr
anq
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per
iod
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rrec
tly
cal
led
40
.00%
21
.43%
0.
00%
50
.00%
77
.37%
74
.05%
62
.40%
69
.92%
% o
f fa
lse
alar
ms
of
tota
l al
arm
s 60
.00%
42
.31%
32
.00%
9.
68%
79
.49%
69
.39%
73
.44%
56
.92%
QP
S
1.11
11
1.00
00
1.05
56
0.27
78
0.50
98
0.53
56
0.75
82
0.50
98
GS
B
0.02
47
0.02
47
0.01
39
0.00
15
0.04
52
0.06
23
0.11
07
0.10
47
50%
% o
f o
bse
rvat
ion
s co
rrec
tly
cal
led
44
.44%
52
.78%
47
.22%
83
.33%
74
.51%
73
.86%
66
.67%
75
.82%
% o
f p
re-c
risi
s p
erio
ds
corr
ectl
y c
alle
d
50.0
0%
68.1
8%
60.7
1%
90.0
0%
50.0
0%
68.1
8%
60.7
1%
90.0
0%
% o
f tr
anq
uil
per
iod
s co
rrec
tly
cal
led
40
.00%
28
.57%
0.
00%
50
.00%
77
.37%
74
.81%
68
.00%
72
.36%
% o
f fa
lse
alar
ms
of
tota
l al
arm
s 60
.00%
40
.00%
32
.00%
10
.00%
79
.49%
68
.75%
70
.18%
55
.74%
QP
S
1.11
11
0.94
44
1.05
56
0.33
33
0.50
98
0.52
29
0.66
67
0.48
37
GS
B
0.02
47
0.01
39
0.01
39
0.00
00
0.04
52
0.05
78
0.07
185
0.08
21
198
7.4. Models Comparison Using a Shorter Crisis Window
In the previous section, three EWS models were exercised to predict Indonesian
currency crises within three new crisis windows, which were shorter than crisis
window of the benchmark models. However, a comparison of their ability to
predict crisis for every crisis window was not done. Nevertheless, based on
these previous exercises, in predicting crisis within a 6-month crisis window,
this study found that with the exception of the ANN model, the prediction
results of the other two models, namely signal and probit models, were
inconsistent and more limited compared to the longer prediction horizons. As
the EWS model is forward looking, ideally warning signals that are sent must
provide sufficient time for policy makers to set up preventive measures. For
this, a 6-month crisis window is too short, particularly for government to act
upon these warning signals. Based on the exercise in the previous section,
except for the ANN model, other models tended to be late in sending warning
signals. For example, the signal model sent warning signals too early, but
because this crisis window was too short, the signals could be categorized as
false alarms.
For the 18-months crisis window, Goldstein et al. (2000) found that their
predictions were similar to the model with a 24-month crisis window. In the
empirical literature in this field, most studies have applied a 24-month crisis
window, while some of them also used a 12-month crisis window as an
alternative. However, they have rarely used the 6-month and 18-month crisis
windows.
Consequently, and following Bussiere and Fratzcher (2002), Kamin et al. (2007),
and Nag and Mitra (1999), in this section, a comparison will only be made for
the performance of these EWS models in predicting currency crises within the
12-months crisis windows. According to Bussiere and Fratzcher (2002), the 12-
months crisis window is an optimal combination that fits two opposing views,
the first of which indicates that economic variables perform poorly towards the
time of crisis, but on the other that policy makers need a fairly long time to be
able to take preventive measures. Consequently, this study also provides a
199
comparison of these models for the other two 6 and 18 months crisis windows,
as seen in the attachments.
As mentioned above, in this section, this study evaluates and compares the
performance of three EWS models based on their ability to predict the
Indonesian currency crises for both in-sample and out-of-sample periods. For
this purpose, Figure 7.11 presents the in-sample time-series probability of a
crisis, while the performance assessments are reported in Table 7.16. On the
other hand, the out-of-sample time-series probabilities of a crisis for these
models are displayed in Figure 7.12, and the performance assessments are
reported in Table 7.17.
7.4.1 In-Sample Prediction Using a 12-month Crisis Window
Based on Figure 7.11, it is found that these models can predict the in-sample
currency crises. However, unlike the ANN model, which is able to predict all
pre-crises periods, the other models even though being able to predict the first
two in-sample crises, have less predictive ability for the last in-sample crisis,
particularly the signal model. Table 7.16 indicates that at Pr*=20% these models
are capable of predicting the 12-months pre-crises periods quite highly at 83%
(signal), 81% (probit) and 100% (ANN). However, when Pr* is increased to 50%,
ANN is still able to predict these pre-crises periods by 100%, while the
prediction ability of the other two models decreases to 58%. In other words,
using Figure 7.11 and Table 7.16, it is found that the ANN model performs very
well in predicting the in-sample pre-crises and is also superior compared to the
signal and probit models.
FIGURE 7.11 In-Sample Prediction
(a)
(b) The p
(c)
0%
20%
40%
60%
80%
100%1
97
1M
01
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M1
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5
200
ample Prediction Using a 12-month Crisis Windows
(a) The Signal model
(b) The probit model
(c) The ANN model
19
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cc12m signal_12m
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cc12m probit_12m
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cc12m ann_12m
month Crisis Windows
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201
This figure also shows that these models send false alarms. This applies
particularly to the signal and probit models. Furthermore, the signal model sent
more false alarms (mainly in the 1970s), but the probit sent more at the
beginning of the 1990s. This in turn makes these models to perform less well in
the prediction of the tranquil periods compared to the ANN model. In addition,
the other performance assessment methods, such as the percentage of
observation correctly predicted, level of accuracy (QPS) and calibration (GSB),
also support these results and point out that ANN still performed better than
the signal and probit models.
TABLE 7.16 In-Sample Evaluation Using a 12-month Crisis Windows
Pr* Assessment methods In-sample (1970/71-1995)
Signal Probit ANN
20%
% of observations correctly called 87.78% 87.67% 99.33%
% of pre-crisis periods correctly called 83.33% 80.56% 100.00%
% of tranquil periods correctly called 88.36% 88.64% 99.24%
% of false alarms of total alarms 51.61% 50.85% 5.26%
QPS 0.2444 0.2467 0.0133
GSB 0.0140 0.0118 0.0001
30%
% of observations correctly called 91.96% 89.67% 100.00%
% of pre-crisis periods correctly called 58.33% 63.89% 100.00%
% of tranquil periods correctly called 96.36% 93.18% 100.00%
% of false alarms of total alarms 32.26% 43.90% 0.00%
QPS 0.1608 0.2067 0.0000
GSB 0.0005 0.0006 0.0000
40%
% of observations correctly called 91.96% 91.67% 100.00%
% of pre-crisis periods correctly called 58.33% 58.33% 100.00%
% of tranquil periods correctly called 96.36% 96.21% 100.00%
% of false alarms of total alarms 32.26% 32.26% 0.00%
QPS 0.1608 0.1667 0.0000
GSB 0.0005 0.0006 0.0000
50%
% of observations correctly called 91.96% 93.00% 100.00%
% of pre-crisis periods correctly called 58.33% 58.33% 100.00%
% of tranquil periods correctly called 96.36% 97.73% 100.00%
% of false alarms of total alarms 32.26% 22.22% 0.00%
QPS 0.1608 0.1400 0.0000
GSB 0.0005 0.0018 0.0000
202
7.4.2. Out-of-Sample Prediction Using a 12-month Crisis Window
This subsection evaluates and compares the performance of these models in
predicting the Asian Financial Crisis in 1997/98. For this purpose, Figure 7.12
presents the out-of-sample time-series probability of a crisis for these three
models from 1996 to 2008. To compare the performance of these models, this
study also uses six performance assessment methods and the results are
reported in Table 7.17.
Basically, these models were able to predict the presence of the Asian Financial
Crisis in Indonesia, as their probability of a crisis increased during the 12-
month pre-crisis period. To see this in more detail and the ability of these
models to predict this crisis and for ease of comparing their performance, this
study uses the percentage of pre-crisis periods correctly captured by the model
presented in Table 7.16. Based on this assessment, it was found that at Pr*=20%,
all models could predict well but that similar to the in-sample prediction, the
ANN model performed better when compared to the other two models. For
example 64% (signal), 59% (probit) and 73% (ANN). Unlike the other models,
when Pr* increased by 10% to 30%, the predictive ability of the signal dropped
dramatically to 18% and remained there even when Pr* was increased to 50%.
Meanwhile for probit and ANN models, their predictive ability decreased
gradually in line with the increase in Pr*, so that when Pr*=50%, the probit
model was only able to predict its pre-crisis period by 27%, while the ANN
model was able to predict by 68%. A prediction result that was much better
than the other models. For more details see Table 7.17.
Another important factor in the early warning system model is the timing of
warning signals. Ideally, this EWS model has to send warning signals before the
occurrence of a crisis, so that policy makers will still have sufficient time to take
preventive actions to overcome these threats.
FIGURE 7.12 Out
0%
20%
40%
60%
80%
100%
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203
Out-of-sample Prediction Using a 12-month
(a) The signal model
(b) The probit model
(c) The ANN model
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cc12m signal_12m
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cc12m probit_12m
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cc12m ann_12m
month Crisis Windows
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204
Regarding the timing of warning signals transmitted by these models, in
Figure 7.12 these models would be expected to send warning signals from
the beginning of their 12-month crisis window, or September 1996, as
highlighted in yellow. However, in this figure, there are no models that were
able to send warning signals from the beginning of their crisis window in
September 1996. While the signal model sent warning signals after three
months, or after December 1996, its probability of a crisis of 29% was not too
significant. However it did send strong warning signals in December 1997
when its probability of a crisis increased to 68%.
Likewise, the probit model was only able to transmit its signal from January
1997, or one month later than the signal model, with the probability of a crisis of
34%. Despite some fluctuations, its probability of a crisis reached 47% in June
1997 before gradually falling. Furthermore, this model sent warning signals of a
crisis after January 1998, with the probability of Indonesia being hit by a crisis
within 12 months reaching 100%.
Regarding the time of the signals transmitted, Figure 7.12 shows that the ANN
model was capable of sending warning signals from early 1996, or a few
months before its prediction horizon, although these warning signals can be
categorized as false signals because no crisis occurred within its crisis window.
However, this model was unable to send its warning signals after the beginning
of its prediction horizon. Although a bit late, the ANN model was capable of
sending warning signals after January 1997 when the probability of a crisis
reached 100%. It dropped to 12% in November 1997 but then rose again to 100%
in January 1998.
20
5
TA
BL
E 7
.17
Ou
t-of
-sam
ple
Eva
luat
ion
Usi
ng
a 12
-mon
th C
risi
s W
indo
w
Thresh
olds
Assessm
ent methods
Out-of-sample
1996-1998
1996-2008
Signal
Probit
ANN
Signal
Probit
ANN
20%
% o
f o
bse
rvat
ion
s co
rrec
tly
cal
led
61
.11%
58
.33%
50
.00%
59
.48%
65
.36%
69
.93%
%
of
pre
-cri
sis
per
iod
s co
rrec
tly
cal
led
63
.64%
59
.09%
72
.73%
63
.64%
59
.09%
72
.73%
%
of
tran
qu
il p
erio
ds
corr
ectl
y c
alle
d
57.1
4%
57.1
4%
14.2
9%
58.7
8%
66.4
1%
69.4
7%
% o
f fa
lse
alar
ms
of
tota
l al
arm
s 30
.00%
31
.58%
42
.86%
79
.41%
77
.19%
71
.43%
Q
PS
0.
7778
0.
8333
1.
0000
0.
8105
0.
6928
0.
6013
G
SB
0.
0062
0.
0139
0.
0556
0.
1808
0.
1047
0.
0988
30%
% o
f o
bse
rvat
ion
s co
rrec
tly
cal
led
50
.00%
52
.78%
50
.00%
86
.93%
67
.32%
72
.55%
%
of
pre
-cri
sis
per
iod
s co
rrec
tly
cal
led
18
.18%
50
.00%
72
.73%
18
.18%
50
.00%
72
.73%
% o
f tr
anq
uil
per
iod
s co
rrec
tly
cal
led
10
0.00
%
57.1
4%
14.2
9%
98.4
7%
70.2
3%
72.5
2%
% o
f fa
lse
alar
ms
of
tota
l al
arm
s 0.
00%
35
.29%
42
.86%
33
.33%
78
.00%
69
.23%
Q
PS
1.
0000
0.
9444
1.
0000
0.
2614
0.
6536
0.
5490
G
SB
0.
5000
0.
0386
0.
0556
0.
0219
0.
0670
0.
0769
40
%
% o
f o
bse
rvat
ion
s co
rrec
tly
cal
led
50
.00%
41
.67%
50
.00%
86
.93%
69
.28%
73
.20%
%
of
pre
-cri
sis
per
iod
s co
rrec
tly
cal
led
18
.18%
31
.82%
68
.18%
18
.18%
31
.82%
68
.18%
%
of
tran
qu
il p
erio
ds
corr
ectl
y c
alle
d
100.
00%
57
.14%
21
.43%
98
.47%
75
.57%
74
.05%
%
of
fals
e al
arm
s o
f to
tal
alar
ms
0.00
%
46.1
5%
42.3
1%
33.3
3%
82.0
5%
69.3
9%
QP
S
1.00
00
1.16
67
1.00
00
0.26
14
0.61
44
0.53
56
GS
B
0.50
00
0.12
50
0.02
47
0.02
19
0.02
47
0.06
23
50%
% o
f o
bse
rvat
ion
s co
rrec
tly
cal
led
50
.00%
38
.89%
52
.78%
86
.93%
69
.28%
73
.86%
%
of
pre
-cri
sis
per
iod
s co
rrec
tly
cal
led
18
.18%
27
.27%
68
.18%
18
.18%
27
.27%
68
.18%
%
of
tran
qu
il p
erio
ds
corr
ectl
y c
alle
d
100.
00%
57
.14%
28
.57%
98
.47%
76
.34%
74
.81%
% o
f fa
lse
alar
ms
of
tota
l al
arm
s 0.
00%
50
.00%
40
.00%
33
.33%
83
.78%
68
.75%
Q
PS
1.
0000
1.
2222
0.
9444
0.
2614
0.
6144
0.
5229
G
SB
0.
5000
0.
1543
0.
0139
0.
0219
0.
0192
0.
0578
206
Despite these models being capable of predicting the Asian Financial Crisis, the
figure also shows they were still sending lots of warning signals even though
by definition this crisis was the only currency crisis in this period. Table 7.17
shows that during the period of crisis from 1996 to 1998, the ANN model sent
more false signals than the other models. For the entire out-of-sample period
from 1996 to 2008, at Pr*=20%, the percentage of false alarms transmitted by the
ANN model was 71%, or the smallest compared to the other models that sent
79% by signal and 77% by probit. However, when Pr* was increased to 50%, the
signal model sent fewer false signals than any other models.
Regarding false alarms, the figure indicates that the probability of a crisis of
these models generally increased after the Asian Financial Crisis until 2000. This
also occurred between mid-2001 and 2002. In addition, the probability of a crisis
of the signal and probit models also increased after the beginning of 2008. As
mentioned in previous chapters, unlike the parametric model (probit model),
one of the biggest criticisms of the non-parametric approach such as the ANN
and signal models is their inability to explain the causality between the
independent and dependent variables, thus making it difficult to explain the
fluctuation of their probability of a crisis.
As a consequence of sending fewer false alarms, the signal model provided
poorer performance than the other models when predicting the tranquil
periods. It also performed poorly in predicting the pre-crisis period, as the
number of months in the tranquil period was much larger than the number of
months in the crisis period. However, the model does perform well when the
whole observation, including crisis and tranquil periods, is used. Similar results
are also found in terms of accuracy and calibration for predicting the whole
observation.
However, because the main objective of the EWS model is predicting crisis
rather than tranquil periods, evaluating and choosing the optimal EWS models
is based on the ability to predict crises and the timing accuracy of warning
signals sent. It has been found that as with the benchmark models, the ANN
207
model performs better than the other models in predicting crises within the
shorter crisis window of 12 months.
7.5. Conclusions
In this chapter three things have been achieved, namely, the full comparison of
the three main models in predicting crises in the 24-month crisis window;
performing the sensitivity tests to see the consistency of the three models in
predicting crises within three shorter crisis windows; and comparing the ability
of these three EWS models for predicting crises within shorter crisis windows.
When comparing the performance of these three EWS models in predicting
crises within the 24-month crisis window, it was found that the performance of
the ANN model was better than signal and probit models for both within
samples from 1970/71 to 1995 and out-of-samples from 1996 to 2008. For
example, in predicting the three in-sample currency crises, these models were
able to predict about 99-100% (ANN model), 75-94% (probit model), and 57-
83% (signal model) for the 24-month pre-crisis period. Similarly for the out-of-
sample crisis - the Asian Financial Crisis - ANN still performed better than the
other models, as it was able to predict the 24-month pre-crisis periods at about
90-97%, while signal and probit models were only able to capture 30-73% and
43-53% of the pre-crisis periods, respectively.
Moreover, in terms of the timing of warning signals sent by these models for
predicting the Asian Financial Crisis in 1997/98, ANN was able to send
warning signals of the presence of this crisis from January 1996, with the
probability of Indonesia having this crisis within 24 months at about 66%, while
the signal model started to send its signals four month later, from April 1996,
with the probability of a crisis at 36%. In contrast, a year later, the probit model
started to warn about this crisis when the probability of a crisis reached 66% in
January 1997.
208
In evaluating the consistency and sensitivity of these models as a response to
the change in the prediction horizon or crisis window, this study applied three
new crisis windows that were shorter than the default or benchmark crisis
window of 24 months, namely 6, 12, and 18 months. As a result all models were
repeated to predict crises within these new crisis windows.
Two signal models were adopted based on two options related to the selection
of leading indicators. The first signal model used the same set of indicators as
the benchmark model, while the second model used the same procedure with
the benchmark model by recalculating the lowest noise-to-signal ratio for each
leading indicator for these three shorter crisis windows. This affected the set of
leading indicators for constructing the composite index that tended to vary
across crisis windows, including the benchmark model.
The results showed that the in-sample prediction capability of these models was
less sensitive to the change in crisis windows, because in general these models
consistently performed well even though they were unable to predict the third
in-sample crisis. In predicting the out-of-sample currency crisis, these models
were however more sensitive to the change in crisis windows, particularly
when using the 30% or more cut-off probability, as their prediction results were
insignificant compared to the benchmark model. A comparison between these
two signal models indicates that the second model generally performed better
than the first model across crisis windows and cut-off probabilities. In addition,
compared to the other crisis windows, their ability to predict crises within the 6-
month crisis window was limited because their maximum probability of a crisis
was 32% (signal 1a) and 45% (signal 1b). When the cut-off-probability increased
to 50%, these models failed to send any warning signals.
For the probit model, the results of the sensitivity tests showed that the
sensitivity and consistency varied depending on the prediction horizons, and
the longer the crisis window was used the more consistent this model was in
predicting crises relative to the benchmark model. This model’s ability to
predict pre-crisis periods tended to increase when the crisis window was
209
expanded and the out-of-sample predicted results for the 18-month crisis
window were even better than the benchmark model.
In contrast, the results of sensitivity tests for the ANN model show that it was
not sensitive to the changes in the crisis window as it was consistently able to
predict the pre-crisis period very well, particularly for its in-sample prediction.
Likewise, for the out-of-sample prediction, the test indicated that it was less
sensitive, but when compared with the in-sample prediction, the out-of-sample
prediction was more sensitive. Although their out-of-sample predictions are not
as good as the benchmark model, these models still consistently performed
well, for they were able to predict more than 50% of the pre-crisis periods for all
crisis windows and cut-off-probabilities of 50-56% (ANN 1), 68-75% (ANN 2)
and 61-75% (ANN 3).
In comparing the performance of these three EWS models in predicting crises
within the 12-month crisis windows, this study found that the results were in
accordance with those of the benchmark model and the ANN model still
performed better than the other two models. These results were also valid for
the comparison of other shorter crisis windows, namely the 6-month and 18-
month crisis windows for both within sample and out-of-sample, as well as all
four levels of cut-off-probabilities. For further details, a comparison of the
ability of these three models in predicting pre-crisis periods within various
prediction horizons for each boundary can be seen in Figures 7.13 and 7.14.
210
FIGURE 7.13 In-Sample Comparison Using Various Crisis Windows and Cut-off
Probabilities, 1970/71-1995
FIGURE 7.14 Out-of-sample Comparison Using Various Crisis Windows and Cut-
off Probabilities, 1996-2008
0%
20%
40%
60%
80%
100%
20% 30% 40% 50% 20% 30% 40% 50% 20% 30% 40% 50% 20% 30% 40% 50%
6-months 12-months 18-months 24-months
In-sample
signal Probit ANN
0,00%
20,00%
40,00%
60,00%
80,00%
100,00%
20% 30% 40% 50% 20% 30% 40% 50% 20% 30% 40% 50% 20% 30% 40% 50%
6-months 12-months 18-months 24-months
Out-of-sample
signal Probit ANN
Appendixes
FIGURE A7.1
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211
7.1 In-Sample Prediction Using a 6-month Crisis Window
(a) The signal model
(b) The probit model
(c) The ANN model
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cc6m probit_6m
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cc6m ann_6m
month Crisis Window
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212
TABLE A7.1 In-Sample Evaluation Using a 6-month Crisis Window
Pr* Assessment methods In-sample (1970/71-1995)
Signal Probit ANN
20%
% of observations correctly called 93.25% 92.33% 98.67%
% of pre-crisis periods correctly called 77.78% 61.11% 100.00%
% of tranquil periods correctly called 94.20% 94.33% 98.58%
% of false alarms of total alarms 54.84% 59.26% 18.18%
QPS 0.1350 0.1533 0.0267
GSB 0.0035 0.0018 0.0004
30%
% of observations correctly called 93.25% 93.67% 99.33%
% of pre-crisis periods correctly called 77.78% 44.44% 100.00%
% of tranquil periods correctly called 94.20% 96.81% 99.29%
% of false alarms of total alarms 54.84% 52.94% 10.00%
QPS 0.1350 0.1267 0.0133
GSB 0.0035 0.0000 0.0001
40%
% of observations correctly called 93.25% 95.00% 99.67%
% of pre-crisis periods correctly called 77.78% 38.89% 100.00%
% of tranquil periods correctly called 94.20% 98.58% 99.65%
% of false alarms of total alarms 54.839% 36.36% 5.26%
QPS 0.1350 0.1000 0.0067
GSB 0.0035 0.0011 0.0000
50%
% of observations correctly called 94.21% 95.00% 99.67%
% of pre-crisis periods correctly called 0.00% 27.78% 100.00%
% of tranquil periods correctly called 100.00% 99.29% 99.65%
% of false alarms of total alarms 0.00% 28.57% 5.26%
QPS 0.1158 0.1000 0.0067
GSB 0.0067 0.0027 0.0000
FIGURE A7.2 Out
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213
Out-of-Sample Prediction Using a 6-month Crisis Window
(a) The signal model
(b) The probit model
(c) The ANN model
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cc6m ann_6m
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21
4
TA
BL
E A
7.2
Ou
t-of
-Sam
ple
Ev
alu
atio
n U
sin
g a
6-m
onth
Cri
sis
Win
dow
Pr*
Assessment methods
Out-of-sample
1996-1998
1996-2008
Signal
Probit
ANN
Signal
Probit
ANN
20%
% o
f o
bse
rvat
ion
s co
rrec
tly
cal
led
75
.00%
55
.56%
47
.22%
86
.93%
74
.51%
73
.86%
%
of
pre
-cri
sis
per
iod
s co
rrec
tly
cal
led
50
.00%
37
.50%
56
.25%
50
.00%
37
.50%
56
.25%
% o
f tr
anq
uil
per
iod
s co
rrec
tly
cal
led
95
.00%
70
.00%
40
.00%
91
.24%
78
.83%
75
.91%
%
of
fals
e al
arm
s o
f to
tal
alar
ms
11.1
1%
50.0
0%
57.1
4%
60.0
0%
82.8
6%
78.5
7%
QP
S
0.50
00
0.88
89
1.05
56
0.26
14
0.50
98
0.52
29
GS
B
0.07
56
0.02
47
0.03
86
0.00
14
0.03
08
0.05
78
30%
% o
f o
bse
rvat
ion
s co
rrec
tly
cal
led
75
.00%
55
.56%
47
.22%
86
.93%
75
.82%
75
.16%
%
of
pre
-cri
sis
per
iod
s co
rrec
tly
cal
led
50
.00%
37
.50%
56
.25%
50
.00%
37
.50%
56
.25%
%
of
tran
qu
il p
erio
ds
corr
ectl
y c
alle
d
95.0
0%
70.0
0%
40.0
0%
91.2
4%
80.2
9%
77.3
7%
% o
f fa
lse
alar
ms
of
tota
l al
arm
s 11
.11%
50
.00%
57
.14%
60
.00%
81
.82%
77
.50%
Q
PS
0.
5000
0.
8889
1.
0556
0.
2614
0.
4837
0.
4967
G
SB
0.
0756
0.
0247
0.
0386
0.
0014
0.
0247
0.
0492
40
%
% o
f o
bse
rvat
ion
s co
rrec
tly
cal
led
75
.00%
55
.56%
44
.44%
86
.93%
75
.82%
74
.51%
%
of
pre
-cri
sis
per
iod
s co
rrec
tly
cal
led
50
.00%
37
.50%
50
.00%
50
.00%
37
.50%
50
.00%
%
of
tran
qu
il p
erio
ds
corr
ectl
y c
alle
d
95.0
0%
70.0
0%
40.0
0%
91.2
4%
80.2
9%
77.3
7%
% o
f fa
lse
alar
ms
of
tota
l al
arm
s 11
.11%
50
.00%
60
.00%
60
.00%
81
.82%
79
.49%
Q
PS
0.
5000
0.
8889
1.
1111
0.
2614
0.
4837
0.
5098
G
SB
0.
0756
0.
0247
0.
0247
0.
0014
0.
0247
0.
0452
50%
% o
f o
bse
rvat
ion
s co
rrec
tly
cal
led
55
.56%
55
.56%
44
.44%
89
.54%
75
.82%
74
.51%
%
of
pre
-cri
sis
per
iod
s co
rrec
tly
cal
led
0.
00%
37
.50%
50
.00%
0.
00%
37
.50%
50
.00%
%
of
tran
qu
il p
erio
ds
corr
ectl
y c
alle
d
100.
00%
70
.00%
40
.00%
10
0.00
%
80.2
9%
77.3
7%
% o
f fa
lse
alar
ms
of
tota
l al
arm
s 0.
00%
50
.00%
60
.00%
0.
00%
81
.82%
79
.49%
Q
PS
0.
8889
0.
8889
1.
1111
0.
2092
0.
4837
0.
5098
G
SB
0.
3951
0.
0247
0.
0247
0.
0219
0.
0247
0.
0452
FIGURE A7.3
0%
20%
40%
60%
80%
100%
19
71
M0
1
19
71
M1
2
19
72
M1
1
19
73
M1
0
19
74
M0
9
0%
20%
40%
60%
80%
100%
19
71
M0
1
19
71
M1
2
19
72
M1
1
19
73
M1
0
19
74
M0
9
0%
20%
40%
60%
80%
100%
19
71
M0
1
19
71
M1
2
19
72
M1
1
19
73
M1
0
19
74
M0
9
215
3 In-Sample Prediction Using a 18-month Crisis Window
(a) The signal model
(b) The probit model
(c) The ANN model
19
74
M0
9
19
75
M0
8
19
76
M0
7
19
77
M0
6
19
78
M0
5
19
79
M0
4
19
80
M0
3
19
81
M0
2
19
82
M0
1
19
82
M1
2
19
83
M1
1
19
84
M1
0
19
85
M0
9
19
86
M0
8
19
87
M0
7
19
88
M0
6
19
89
M0
5
19
90
M0
4
cc18m signal_18m
19
74
M0
9
19
75
M0
8
19
76
M0
7
19
77
M0
6
19
78
M0
5
19
79
M0
4
19
80
M0
3
19
81
M0
2
19
82
M0
1
19
82
M1
2
19
83
M1
1
19
84
M1
0
19
85
M0
9
19
86
M0
8
19
87
M0
7
19
88
M0
6
19
89
M0
5
19
90
M0
4
cc18m probit_18m
19
74
M0
9
19
75
M0
8
19
76
M0
7
19
77
M0
6
19
78
M0
5
19
79
M0
4
19
80
M0
3
19
81
M0
2
19
82
M0
1
19
82
M1
2
19
83
M1
1
19
84
M1
0
19
85
M0
9
19
86
M0
8
19
87
M0
7
19
88
M0
6
19
89
M0
5
19
90
M0
4
cc18m ann_18m
Crisis Window
19
90
M0
4
19
91
M0
3
19
92
M0
2
19
93
M0
1
19
93
M1
2
19
94
M1
1
19
95
M1
0
19
90
M0
4
19
91
M0
3
19
92
M0
2
19
93
M0
1
19
93
M1
2
19
94
M1
1
19
95
M1
0
19
90
M0
4
19
91
M0
3
19
92
M0
2
19
93
M0
1
19
93
M1
2
19
94
M1
1
19
95
M1
0
216
TABLE A7.3 In-Sample Evaluation Using a 18-month Crisis Window
Pr* Assessment methods In-sample (1970/71-1995)
Signal Probit ANN
20%
% of observations correctly called 80.71% 87.00% 98.00%
% of pre-crisis periods correctly called 79.63% 87.04% 100.00%
% of tranquil periods correctly called 80.93% 86.99% 97.56%
% of false alarms of total alarms 53.26% 40.51% 10.00%
QPS 0.3859 0.2600 0.0400
GSB 0.0299 0.0139 0.0008
30%
% of observations correctly called 85.85% 90.67% 99.33%
% of pre-crisis periods correctly called 66.67% 85.19% 98.15%
% of tranquil periods correctly called 89.88% 91.87% 99.59%
% of false alarms of total alarms 41.94% 30.30% 1.85%
QPS 0.2830 0.1867 0.0133
GSB 0.0013 0.0032 0.0000
40%
% of observations correctly called 88.75% 90.67% 99.67%
% of pre-crisis periods correctly called 46.30% 72.22% 98.15%
% of tranquil periods correctly called 97.67% 94.72% 100.00%
% of false alarms of total alarms 19.35% 25.00% 0.00%
QPS 0.2251 0.1867 0.0067
GSB 0.0109 0.0001 0.0000
50%
% of observations correctly called 88.75% 90.67% 99.67%
% of pre-crisis periods correctly called 46.30% 64.81% 98.15%
% of tranquil periods correctly called 97.67% 96.34% 100.00%
% of false alarms of total alarms 19.35% 20.45% 0.00%
QPS 0.2251 0.1867 0.0067
GSB 0.0109 0.0022 0.0000
FIGURE A7.4 Out
0%
20%
40%
60%
80%
100%
19
96
M0
1
19
96
M0
7
19
97
M0
1
19
97
M0
7
19
98
M0
1
0%
20%
40%
60%
80%
100%
19
96
M0
1
19
96
M0
7
19
97
M0
1
19
97
M0
7
19
98
M0
1
0%
20%
40%
60%
80%
100%
19
96
M0
1
19
96
M0
7
19
97
M0
1
19
97
M0
7
19
98
M0
1
217
Out-of-Sample Prediction Using a 18-month Crisis Window
(a) The signal model
(b) The probit model
(c) The ANN model
19
98
M0
1
19
98
M0
7
19
99
M0
1
19
99
M0
7
20
00
M0
1
20
00
M0
7
20
01
M0
1
20
01
M0
7
20
02
M0
1
20
02
M0
7
20
03
M0
1
20
03
M0
7
20
04
M0
1
20
04
M0
7
20
05
M0
1
20
05
M0
7
cc18m signal_18m
19
98
M0
1
19
98
M0
7
19
99
M0
1
19
99
M0
7
20
00
M0
1
20
00
M0
7
20
01
M0
1
20
01
M0
7
20
02
M0
1
20
02
M0
7
20
03
M0
1
20
03
M0
7
20
04
M0
1
20
04
M0
7
20
05
M0
1
20
05
M0
7
cc18m probit_18m
19
98
M0
1
19
98
M0
7
19
99
M0
1
19
99
M0
7
20
00
M0
1
20
00
M0
7
20
01
M0
1
20
01
M0
7
20
02
M0
1
20
02
M0
7
20
03
M0
1
20
03
M0
7
20
04
M0
1
20
04
M0
7
20
05
M0
1
20
05
M0
7
cc18m ann_18m
month Crisis Window
20
06
M0
1
20
06
M0
7
20
07
M0
1
20
07
M0
7
20
08
M0
1
20
08
M0
7
20
06
M0
1
20
06
M0
7
20
07
M0
1
20
07
M0
7
20
08
M0
1
20
08
M0
7
20
06
M0
1
20
06
M0
7
20
07
M0
1
20
07
M0
7
20
08
M0
1
20
08
M0
7
21
8
TA
BL
E A
7.4
Ou
t-of
-Sam
ple
Ev
alu
atio
n U
sin
g a
18
-mon
th C
risi
s W
ind
ow
Pr*
Assessment methods
Out-of-sample
1996-1998
1996-2008
Signal
Probit
ANN
Signal
Probit
ANN
20%
% o
f o
bse
rvat
ion
s co
rrec
tly
cal
led
63
.89%
50
.00%
58
.33%
50
.33%
52
.94%
58
.82%
%
of
pre
-cri
sis
per
iod
s co
rrec
tly
cal
led
57
.14%
57
.14%
75
.00%
57
.14%
57
.14%
75
.00%
% o
f tr
anq
uil
per
iod
s co
rrec
tly
cal
led
87
.50%
25
.00%
0.
00%
48
.80%
52
.00%
55
.20%
%
of
fals
e al
arm
s o
f to
tal
alar
ms
5.88
%
27.2
7%
27.5
9%
80.0
0%
78.9
5%
72.7
3%
QP
S
0.72
22
1.00
00
0.83
33
0.99
35
0.94
12
0.82
35
GS
B
0.18
67
0.05
56
0.00
15
0.23
10
0.19
69
0.20
51
30%
% o
f o
bse
rvat
ion
s co
rrec
tly
cal
led
58
.33%
44
.44%
47
.22%
62
.75%
56
.86%
60
.13%
%
of
pre
-cri
sis
per
iod
s co
rrec
tly
cal
led
46
.43%
50
.00%
60
.71%
46
.43%
50
.00%
60
.71%
%
of
tran
qu
il p
erio
ds
corr
ectl
y c
alle
d
100.
00%
25
.00%
0.
00%
66
.40%
58
.40%
60
.00%
% o
f fa
lse
alar
ms
of
tota
l al
arm
s 0.
00%
30
.00%
32
.00%
76
.36%
78
.79%
74
.63%
Q
PS
0.
8333
1.
1111
1.
0556
0.
7451
0.
8628
0.
7974
G
SB
0.
3472
0.
0988
0.
0139
0.
0623
0.
1234
0.
1300
40
%
% o
f o
bse
rvat
ion
s co
rrec
tly
cal
led
30
.56%
41
.67%
47
.22%
73
.20%
60
.13%
62
.09%
%
of
pre
-cri
sis
per
iod
s co
rrec
tly
cal
led
10
.71%
46
.43%
60
.71%
10
.71%
46
.43%
60
.71%
%
of
tran
qu
il p
erio
ds
corr
ectl
y c
alle
d
100.
00%
25
.00%
0.
00%
87
.20%
63
.20%
62
.40%
%
of
fals
e al
arm
s o
f to
tal
alar
ms
0.00
%
31.5
8%
32.0
0%
84.2
1%
77.9
7%
73.4
4%
QP
S
1.38
89
1.16
67
1.05
56
0.53
59
0.79
74
0.75
82
GS
B
0.96
45
0.12
50
0.01
39
0.00
69
0.08
21
0.11
07
50%
% o
f o
bse
rvat
ion
s co
rrec
tly
cal
led
30
.56%
41
.67%
47
.22%
73
.20%
64
.71%
66
.67%
%
of
pre
-cri
sis
per
iod
s co
rrec
tly
cal
led
10
.71%
46
.43%
60
.71%
10
.71%
46
.43%
60
.71%
%
of
tran
qu
il p
erio
ds
corr
ectl
y c
alle
d
100.
00%
25
.00%
0.
00%
87
.20%
68
.80%
68
.00%
%
of
fals
e al
arm
s o
f to
tal
alar
ms
0.00
%
31.5
8%
32.0
0%
84.2
1%
75.0
0%
70.1
8%
QP
S
1.38
89
1.16
67
1.05
56
0.53
59
0.70
59
0.66
67
GS
B
0.96
45
0.12
50
0.01
39
0.00
69
0.04
921
0.07
185
219
CHAPTER 8
CONCLUSIONS
8.1. The Main Findings
The presence and huge impact of currency crises, particularly in the mid-1990s,
encouraged the development of early warning system (EWS) models to predict
these events. This study adds to those efforts by developing three EWS models
for Indonesia, namely the signal approach, the discrete choice probit/logit, and
the artificial neural network (ANN) models. A brief summary of the application
of these three EWS models is also made.
First, following Kaminsky et al. (1998), this study determined that Indonesia
had three currency crises within the sample period (1970-1995) and one
currency crisis, the 1997/98 Asian Financial Crisis, that occurred in the out of
sample period (1996-1998). In predicting these currency crises, the signal
approach was able to predict the 24-month pre-crises periods both in the within
and out-of-sample periods, with the optimum results being obtained at the 30%
cut-off probability point. The weakness in the domestic sector was found to be
the main underlying factor for Indonesia’s currency crises and, based on sector-
specific analysis, it was the financial sector that proved the most dominant in
this respect. Even though it had a limitation when predicting the third in-
sample crisis, this approach was less sensitive to examining the change in crisis
windows, but it consistently performed well when predicting crises within the
sample. However, its out-of-sample prediction was more sensitive, its
predictions not being consistent compared to the benchmark model.
Second, in applying the discrete choice probit/logit model, this study used a set
of explanatory variables based on the top ten indicators when using the noise-
to-signal ratio from the signal approach. Based on the regression results, five
determinant factors were highlighted, namely the short-term capital flows to
GDP, M1 to GDP, loans to deposits, real effective exchange rates and exports,
220
with the short-term capital flows to GDP being a main contributor when
determining the probability of a crisis in Indonesia. In predicting the
Indonesian currency crises, this model was also able to predict both in-sample
and out-of-sample currency crises within the 24-month crisis window. The
sensitivity and consistency of this model varied depending on the prediction
horizons. The longer the crisis window the more consistent was this model in
predicting crises relative to the benchmark model. The performance of this
model in predicting pre-crisis periods tended to increase when the crisis
window was expanded, while the out-of-sample performance was even better
than the benchmark model when predicting results for the 18-month crisis
window.
Third, in predicting currency crises, the ANN model used the same set of input
neurons as the probit model, and showed that the real effective exchange rate
and the 12 months change of loans to deposits were the main factors
contributing towards the probability of a crisis. This model performed well
when predicting the 24-month pre-crises periods for both in-sample and out-of-
sample scenarios. Furthermore, the results of the sensitivity test indicated that
this model was not sensitive to the changes in the crisis window, as it was
consistently able to predict pre-crisis periods, particularly for its in-sample
predictions. Likewise, for the out-of-sample predictions, this test indicated that
the model was less sensitive, but that when compared with the in-sample
prediction, the out-of-sample prediction was more sensitive.
Finally, in comparing the performance of these three EWS models in predicting
currency crises within the 24-month crisis window, it was found that the ANN
model performed better than the other models for both in-sample and out-of-
sample periods. Similar results are found when predictions were made within
shorter crisis windows, over 6, 12 and 18 months, at all level of cut-off
probabilities, these being set at 20, 30, 40 and 50%.
221
8.2. Directions for Future Research
There are several ways in which these EWS models can be improved and
extended in the future. Even though these EWS models were able to predict the
Indonesian currency crises, they also sent lots of false signals, this being
particularly noticeable during the transition and recovery period from 1999 to
2000, during major political events, and during the Global Financial Crisis in
2008. The findings indicate that these EWS models cannot distinguish between
currency crises and other vulnerabilities including political distresses. As this
study only considers the economic and financial variables, so adding variables
such as political risks and the contagion effects may improve the performance
of these models.
The way to define a crisis is also crucial, as the application of the method used
to define a crisis in this study was unable to capture any currency crises after
the Asian Financial Crisis. This was so even though Indonesia experienced
some economic turbulence, particularly during the period of high commodity
prices in 2005, and also during the presence of the global financial crisis at the
end of 2008. One possibility would be to add another variable in the capital
market index. The rationale for adding this variable is that movement in the
capital market index can affect the movement of a domestic currency because of
the integration of capital markets and the trend towards financial liberalization.
222
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