05 res bhanumurthy

VIKALPA VOLUME 38 NO 4 OCTOBER - DECEMBER 2013 13

R E S E A R C H

includes research articles thatfocus on the analysis and

resolution of managerial andacademic issues based on

analytical and empirical or caseresearch

ExecutiveSummary

Measurement of International CurrencyCrises: A Panel Data Approach usingComposite Indices

K V Bhanu Murthy and Anjala Kalsie

KEY WORDS

International Currency Crises

Composite Index

Panel Regression

Asian Crises

Principal ComponentAnalysis

The literature on crisis has often developed indices for measurement of crisis. The

indices that have been developed in the earlier studies suffer from three problems:

Conceptually, they include only exchange related variables and not other relevant

variables that are crucial for international trade and international finance.

The extant studies do not use a causal framework as a methodology for the selec-

tion of variable.

Empirically, they do not use more evolved statistical tools such as Principal Com-

ponent Analysis for constructing a composite index.

This paper seeks to measure the international currency crisis of 1997 in Asia. It has

taken the case of the A5 countries in 1997 and has developed a methodology meant to

measure and explain currency crisis. The study has constructed composite indices for

capturing the causes macro-economic and financial as well as an index of crisis. It

uses Jha & Murthys (2006) approach for constructing composite indices. It combines

continuous and discrete approaches for defining and measuring crises and uses India

as a control which enables international and inter-temporal comparisons during

crisis.

An attempt to explain a widespread and complex phenomenon in terms of a single

dependent variable would be incomplete and partial, where the dependent variables

which represents the crisis are themselves a conglomerate of many factors. Since it is a

complex phenomenon, it cannot be represented by one single variable. Moreover, these

variables tend to be correlated.

With the help of two composite indices one for financial variables and another for

macro variables, a fixed effects panel regression model is developed for explaining the

crisis. The paper measures the decomposition effects of causal financial and macro

variables on the crises.

It has been found that Index of crises consists of exports, exchange rate, and interest

rate. The financial index contains risk rating, domestic financing, and stock traded.

The index of macro variables is based on GDP, capital formation, and budget balance.

Macro index negatively influences crisis while financial index influences crisis posi-

tively.

India as the base country clearly brings out the contrast between crises affected coun-

tries and neutral countries with the help of this methodology. The crisis window

shows up very clearly, as it combines a discrete and continuous definition.

Finally, the differences amongst the five Asian countries during crises are also brought

out by this methodology.

14

The 1990s were marked by a number of rather un-

usual financial and economic crises, such as the

Mexican Peso Crisis of 1994-95 and the Asian Cri-

sis of 1997. While these crises ranged from the garden

variety of currency crises to rather esoteric real estate

bubbles, studies of such crises exhibit empirical and theo-

retical commonalties. The literature distinguishes three

varieties of financial crises: currency crises, banking cri-

ses, and debt crises. The analysis in this study is prima-

rily focused on the currency crises. The task at hand is to

analyse and measure the currency crises in the A5 coun-

tries1 during 1997.

A currency crisis is an episode of intense foreign exchange

market pressure. It can be defined simply as an incident

in which a country experiences a substantial nominal

devaluation or depreciation. This criterion, however,

would exclude instances where a currency came under

severe pressure but the authorities successfully defended

it, by intervening heavily in the foreign exchange market,

by raising interest rates sharply, or by both. Extant ap-

proaches have sometimes constructed an index of specu-

lative market pressures (Kaminsky, Lizondo, & Reinhart,

1998; Edison, 2000; Goldstein, Kaminsky, & Reinhart,

2000).

The indices that have been developed in the earlier stud-

ies suffer from three problems:

1. Conceptually, they include only the exchange-related

variables2 and not the other relevant variables that

are crucial for international trade and international

finance.

2. They do not use causal framework as a methodology

for the selection of variable.

3. Empirically, they do not use more evolved statistical

tools such as Principal Component Analysis for con-

structing a composite index.

This paper is a part of a larger study that looks into a new

approach to measure and analyse international currency

crises. A crucial part of the study is to develop a set of

new composite indices, for both causal variables and

impacted/dependent variables, so as to correlate them in

a regression framework. These indices are based on a large

number of variables and involve a three-stage procedure,

which shall be discussed later.

RATIONALE

An attempt to explain a widespread and complex phe-

nomenon in terms of a single dependent variable would

be incomplete and partial, if the dependent variables

which represent the crisis are themselves a conglomerate

of many factors. Since a crisis is a complex phenomenon,

it cannot be represented by one single variable. Moreover,

the variables tend to be correlated. Thus, the ordinary

regression framework results in the problem of multi-

collinearity. Therefore, it is necessary to measure the phe-

nomenon of the crisis with the help of composite indices,

which would adequately represent the complex phenom-

enon. This applies to both the causes and the effects of a

crisis.

Different studies have identified a variety of factors re-

lated to crisis. This study conducts a causality test to de-

termine which of these factors are causes and which are

effects. The final selection of variables is done on the ba-

sis of an elaborate procedure, which ensures that the vari-

ables which are entering in the construction of the index

of crisis are the ones that are theoretically relevant, as

they are drawn from extant studies and are empirically

sound as they are tested for causality. In addition, they

are appropriate because they have been checked for data

redundancy.

CONCEPTUAL ISSUES

Prior Procedure

Several steps were followed as a part of the larger study

to ensure the above considerations:

1. A literature survey of empirical and conceptual stud-

ies (Moreno, 1995; Berg & Pattillo, 1999; Frankel &

Rose, 1996) was undertaken on the basis of which a

data set consisting of a large number of crises vari-

ables (30 variables including financial and macro vari-

ables) could be arrived at.

2. The set of available variables were checked for data

redundancy. Many defined variables in the data set

were different versions of the same variable. The au-

thors used their judgment to drop a certain version

and retain another version. For instance, if a variable

was defined in terms of PPP $, US $ or local currency

units, only one of them was chosen.

1 Malaysia, Philippines, Korea, Thailand, and Indonesia.2 Weighted average of ER changes, weighted average of RER

changes, Reserves changes, and Interest rate changes.

MEASUREMENT OF INTERNATIONAL CURRENCY CRISES: A PANEL DATA APPROACH ...


3. A correlation exercise was carried out on these 30 vari-

ables. The purpose of this exercise was to establish

that crisis variables were ordinarily correlated3.

4. A dummy variables exercise4 was also undertaken

wherein the data series for six countries for each of the

30 variables were tested to see whether there was any

structural break during 1997 and 1998, which was

the crises window of the Asian currency crisis. How-

ever, the prior correlation analysis and dummy vari-

able exercise did not indicate anything about the

causality amongst the variables. The dummy variable

exercise is a univariate analysis that does not capture

the complexity of the phenomenon.

Measurement of Crisis

After having undertaken the above empirical steps, the

authors proceeded with the measurement of crises. The

first part of the measurement exercise consisted of con-

struction and measurement of the indices while in the

second part, these indices were used to model and pre-

dict the crises and predict.

Crisis Definition

In certain studies, the crisis variable itself has been de-

fined in discrete terms (Eliasson & Krevter, 2000) with the

understanding that the dependent variable had a built-

in discrete change or kink. It should not be forgotten that

the dependent variable is an effect. The authors under-

standing is that whatever change comes in the depend-

ent variable is on account of the causal variables,

including changes in the intercept, because the intercept

also contributes towards the explanatory power of the

equation. In extant literature, the distinction between dis-

crete and continuous crises definition has been captured

through either discrete or continuous dependent variable.

In a causal framework, the mechanism to capture dis-

crete change in the phenomenon has to necessarily rest

upon the causal variables and not the dependent vari-

ables. Accordingly, this study follows the methodology

that, the impetus to discrete change, during crises, is

caused by the indicator of the crises and would necessar-

ily come from the causal variables. This methodology has

been tailored in such a manner that it does not pre-sup-

pose the nature of dependent variable that represents cri-

ses. On the other hand, the explanatory variables are so

endowed that they are capable of inducing discrete change

in the indicator of crises.

The methodology used in this study captures and meas-

ures the discrete change through the independent vari-

able and not through the dependent variable. The

argument is that certain continuous changes can be cap-

tured through the causal variables, which lead up to the

crises. This continuous change is manifest in the volatil-

ity of the crises variables. In effect, it implies that those

countries which were crises-ridden had experienced a

continuous trend of volatility to the extent that this built

up continuously to discrete change resulting in the cri-

ses.

The merit in using a continuous crisis definition lies in

its ability to capture both the continuous influence on the

crises variable (dependent variable) as well as its ability

to explain discrete change which is occurring in the cri-

ses variables during crises, due to the causal variable(s).

Crises Window

The years 1997 and 1998 were taken as the crises win-

dow. The crises developed in November 1997 and peaked

in 1998 in many countries. Therefore, neither 1997 nor

1998 could be ignored. This was vindicated by the dummy

variable exercise which showed a significant structural

break across variables during this crises window (Malik,

2008). Some of the extant studies have used monthly data

and defined the crises window in terms of particular

months. As this study was using annual data, it was nei-

ther possible to have a crises window that pinpointed the

precise period of crises, nor was there any interest in the

process of the crises. Therefore, the crises window in this

study is defined in annual terms.

Relevance of Control

An important issue of research design was the introduc-

tion of a control. In the case of extant studies, with a dis-

crete crises definition, the control was established with

reference to the pre-crises period, since a control is meant

to represent a normal period or normal observation. In

the present study, by control is meant a benchmark

country that was not affected by the crises. For identify-

ing the control, dummy variable exercise was used,

wherein it was found that in the case of India, none of the

relevant variables (those variables which were identified

3 The result of correlation exercise is not reported.4 The result of dummy variable exercise is not reported.

16

through literature review) were affected by the crises; and

therefore, India was chosen as the control. In other words,

none of the variables showed any structural break, dur-

ing the crisis period 1997 and 1998, in India. Such an

approach has the advantage of allowing both inter-tem-

poral as well as international comparisons. The extant

studies permit only inter-temporal comparisons (Malik,

2008).

Since India was established as a control, it was reason-

able to expect that all the relevant variables would dis-

play a normal behaviour in terms of cause and effect.

Hence the causality tests were applied to the relevant

variables only in the case of India.

LITERATURE REVIEW

In view of the complex set of issues involved in the litera-

ture review, the discussion is structured in the form of a

table (See Appendix).

METHODOLOGY

The data was taken from the World Development Report

and World Development Indicators (World Bank) for the

years 1987 to 2002. To account for such a conception of

the phenomenon of crises and causes of the phenomenon,

there was a need for evolving an appropriate methodol-

ogy. The two most desirable features of the methodology

are that, firstly, it should capture the volatility or vari-

ance in the relevant variables, because it is this volatility

that leads up to and results in crisis; secondly, it should

enable working with a large number of related variables

because a crisis according to our understanding is a com-

plex phenomenon resulting from a large number of inter-

correlated variables.

The third dimension of methodology is that given the con-

straints of the data point and degree of freedom, the meth-

odology should allow working with a parsimonious set

of variables. The methodology which possesses all these

features is Principal Component Analysis (PCA). Unlike

Ordinary Least Squares (OLS), wherein the procedure is

to minimize the sum of the squares of deviations, in the

case of PCA, the procedure is to maximize the variance.

Another feature of PCA is that it segregates inter-corre-

lated variables into the separate orthogonal factors or prin-

cipal components. Thirdly, PCA can be used for develop-

ing a composite index which collapses a set of variables

into a single variable that represents a complex phenom-

enon like currency crisis.

Procedure of Estimation

The empirical procedure involved five distinct steps:

1. Granger causality test was applied to the data on In-

dia in respect of all the relevant variables to identify

the causal variables and those that they impacted. This

was done in case of India, since it was the control.

2. Correlation analysis was used to segregate the vari-

ables into impacted or dependent variable and causal

or explanatory or independent variables. Once the vari-

ables were separated into dependent and independ-

ent variables, independent variables were found to be

correlated.

3. To deal with this problem, Principal Component

Analysis was applied. PCA helped in (a) data reduc-

tion and (b) making the dependent variables

uncorrelated.

4. The next step was the formation of a composite index.

It helped in representing the crises phenomenon

which was manifest in a large number of variables.

This is the unique feature of this study.

5. The next step was to specify the model which helped

in merging the two methodological issues, namely

measuring of the crises and comparing the crises-hit

countries with a non-crises hit country. Merging was

done by using panel regression and treating India as

a control.

Causality Test

For developing a causal framework, it is essential to adopt

a procedure by which causal variables can be distin-

guished from impacted/dependent variables. Once the

set of crises variables are sorted, through this procedure,

it would be possible to develop an index of crisis.

In the true spirit, causality test indicates the precedence

of one variable over the other; it is therefore sometimes

cautioned that the result of such tests may not be inter-

preted as cause and effect relationship. Here the authors

would like to point out that the present study is not de-

pendent on the Granger Causality Test. After using the

test and sorting the variables as causal and impacted

variables, a structured causal framework was used with

the appropriate regression technique for establishing

cause and effect.

Granger Causality Test: For carrying out the granger cau-

sality test, the following two equations were estimated

for all the variables:



Ho: X does not cause Y.

Ho: Y does not cause X.

We test the null hypothesis against an F stat, namely,

F= {(RSSr-RSSur)/m}/ (RSSur/ (n-k))

The degrees of freedom are m and (n-k).

The restrictions are respectively:

ai = 0 and di = 0

Yt = ai Xti + bi Yti + u1t (1)

Xt= ci Xti + di Yti + u2t (2)

i =1, 2

RSSr =Residual sum of square restricted

RSSur = Residual sum of square unrestricted

m = Number of linear restrictions

n = Number of observation

k = Number of parameters in the unrestricted regression

It was also ensured that there was no two-way causality

among the relevant variables. As a consequence of the

causality test, three sets of variables could be identified:

(i) pure causal variable; (ii) pure impacted variable; and

(iii) common variables which alternatively appeared as

causal and impacted variables, although not as two-way

causality.

Correlation Matrix

As mentioned earlier, the correlation exercise was done

with a view to sorting the causal and impacted variables.

After identifying the causal and impacted variables,

through the causality test, some variables were found to

be common, that is, they were both impacted as well as

causal variables thus making it difficult to decide which

variables to be selected for constructing an index of crisis.

To solve this problem, correlation matrix was used.

There are two objectives of studying the correlation ma-

trix:

To segregate the set of dependent and independent

variables

To identify a set of crisis variables.

Principal Component Analysis

Principal Components Analysis (henceforth PCA)

(Lewis-Beck, 1994) is based on a linear transformation of

the variables so that they are orthogonal to each other;

hence, no information contained in the points in the event

space is lost. The normality assumption is not essential

in PCA and with such a dispersed set of outcomes, this

feature is useful. PCA is ideally suited because it maxi-

mizes the variance rather than minimizing the least square

distance and this study is interested in capturing vari-

ance because the authors believe that volatility is the ba-

sic cause of a crisis. Since the objective is to develop a

composite Index of Crisis and relate it to two other indi-

ces of financial variables and macro variables, there is a

need to choose the essential variables and arrive at rela-

tive weights for the purpose of consolidating these vari-

ables into a single index. This is facilitated by PCA.

PCA linearly transforms an original set of variables into

a smaller set of uncorrelated variables representing most

of the information in the following form:

(3)

The first principal component is defined such that the

variance of y1 is maximized. Consider the p random vari-

ables x1, x2,.. xp subject to the constraint that the sum of

squared weights is equal to 1, i.e. . If the vari-

ance of y1 is maximized, then the sum of the squared cor-

relations, i.e. is also maximized. PCA finds the

optimal weight vector (a11, a12, a1p) and the associated

variance of y1 (which is denoted as 1).

If the objective is a simple summary of the information

contained in the raw data, the use of component scores is

desirable. It is possible to represent the components ex-

actly from the combination of raw variables. The scores

are obtained by combining the raw variables with weights

that are proportional to their component loadings. In this

case, the component scores were used for determining

the weight of each of the raw variables in constructing a

composite index. As more and more components are ex-

tracted, the measure of the explanatory power increases

but it is necessary to strike a balance between parsimony

and explanatory power.

The goal of PCA is to reveal how different variables change

in relation to each other, or how they are associated. This

is achieved by transforming correlated original variables

into a new set of uncorrelated (orthogonal) underlying

variables (termed principal components) using the

covariance matrix, or its standardized form the correla-

18

5 All relevant variables have been expressed as a proportion ofGDP.

tion matrix. The lack of correlation in the principal com-

ponents is a useful property because it means that the

principal components are measuring different statisti-

cal dimensions in the data. The new variables are a lin-

ear combination of the original ones and are sorted into

descending order according to the amount of variance

that they account for in the original set of variables. Each

new variable accounts for as much of the remaining total

variance of the original data as possible. Cumulatively,

all the new variables account for 100 percent of the varia-

tion. PCA involves calculating the eigen values and their

corresponding eigen vectors of the covariance matrix or

correlation matrix. Each eigen value represents the total

remaining variance that the corresponding new variable

accounts for. The expectation from conducting PCA is

that correlations among original variables are large

enough so that the first few new variables or principal

components account for most of the variance. If this holds,

no essential insight is lost by further analysis or deci-

sion-making, and parsimony and clarity in the structure

of the relationships are achieved. Each factor is a combi-

nation of variables which are correlated with the princi-

pal component.

This methodology has two purposes. As reported early,

both the sets of macro and financial variables were corre-

lated within each set. Under such circumstance, it is not

possible to use the variables in a regression framework

on account of multicollinearity. Secondly, when there are

a large number of impacted variables, they need to be

collapsed into a single dependent variable (Jha & Murthy,

2006).

There is a relevance of using PCA analysis in the

modeling. It allows for data reduction. The reduced data

set would contain the maximum information in all the

variables, which were being considered as dependent

variable. As a result of PCA, the reduced data set consists

of variables which were not correlated to each other, since

the principal components are orthogonal (perpendicu-

lar) to each other.

Finally, the PCA methodology enables us to construct a

composite index. The crux of this methodology is to rep-

resent complex interrelated phenomena such as a crisis

with the help of a single composite index, which could

act as a unique dependent variable. It may be argued that

there are many other factors that influence crises, but our

methodology ensures that the variables which were cho-

sen to construct crises index, effectively represent the im-

pact of all the crises variables. Since the principal varia-

bles are highly correlated to the principal components,

they contain the same information. One measure of the

explanatory power of the index formed by this procedure

is given by the variation explained by the retained princi-

pal component.

Composite Index

Method for Construction of the Index

The main aim of this empirical work is to evolve a com-

posite Index of Crisis (IOC). Hence, there is a need to

choose the essential variables by a data reduction proce-

dure and arrive at relative weights for the purpose of con-

solidating these variables into a single index.

(4)

Xj = Retained variables

Wj = Component scores (weights)

j= 1, 2, 3.

For determining the weights, the Joliffe criterion was used.

According to this procedure, those principal variables

whose component scores were the highest with respect to

the retained components were retained. The process in-

volved a seriatim selection of variables along with their

scores moving from the highest score in the first compo-

nent to the next components and selecting the variable

with its highest score, and so on.

Scale and Code

It must be ensured that the index does not suffer on ac-

count of problems relating to scale and code. The prob-

lem of scale arises out of the difference in scale of the

variables that were components of the composite index.

In this case, the problem of the scale was handled by nor-

malizing the variables5. As a result, variables were ex-

pressed in proportionate terms. The code of the variable

refers to the interpretation of the direction of change with

respect to the value or the measure of the index. For in-

stance, a high number in the index should represent an

increase in the phenomena that the composite index stands

for and higher number should also be generated by the



higher value of the component of the index, which im-

plies a rise in the phenomena. For instance, if exchange

rate is expressed as a local currency unit per dollar, then

a sharp devaluation or depreciation of the currency is

expected during the crises. Hence, the magnitude of the

variable should rise. It is therefore necessary to have con-

sistency between the magnitude of the code and the inter-

pretation of direction of change of the phenomenon, on

the one hand, and consistency within the code in relation

to the individual variables that constitute the index, on

the other. Therefore, a composite index would be repre-

sentative only if the components of the index are repre-

sentative and both the scale and the code are consistent

with the value of the index that is generated. It should

yield a unique and consistent interpretation of the index.

Advantage of the Composite Index

To ascertain whether composite indices function better

than the individual variables, a regression equation was

estimated by including the principal variable directly in

the regression6. The results were not satisfactory, and to

the contrary, a composite index performed better. Appar-

ently, the complexity of the phenomenon was better rep-

resented by a composite index that represented the

combined information content. At the same time it re-

duced the number of variables and permitted higher de-

grees of freedom.

Panel Data Analysis

A methodology which allowed a combination of a con-

tinuous and discrete crisis definition, was adopted. The

intention of this paper is to capture a mix of the impact of

continuous independent variables and fixed effects in

terms of crisis window (discrete effects), as well as coun-

try effects in the form of country dummies. This is an es-

sential part of the research design and objective. Therefore,

the need for a random effects model was not justifiable. In

general, random effects models are meant for capturing

generalized least square (GLS) estimators which meas-

ure the generalized effects and not the fixed effects (coun-

try effects) as has been the purpose of this study.

A fixed effects panel model was used where the slope

coefficients were constant, but the intercept varied over

country as well as time. This study constructs a model

which includes n-1 county dummies but only one time

dummy which represents the crises period (CW). Al-

though there are three periods (pre-crises, during crises,

or post-crises ), only one time dummy was included in

the final model instead of two because the focus of this

study was on crises period. The idea was to have more

degrees of freedom. The recovery period was not so much

of interest to the authors as the crises period. The general

form of the equation that was estimated is as follows:

IOCit = A +b1* (CW) t + b2d2 + b3d3 + b4d4 +b5d5 + b6d6 + b7* X1it + b8* X2it+U1it (5)

IOCt = Dependent variable = Log (Index of Crisis)

A = intercept

b1 = Coefficient of crisis window.

b2 = Intercept dummy for Thailand

b3 = Intercept dummy for Philippines

b4 = Intercept dummy for Korea Republic

b5 = Intercept dummy for Indonesia

b6 = Intercept dummy for Malaysia

(CW)t = Dummy for crisis window: 0 for all years & 1

for 1997, 1998

b7 = Elasticity coefficient for index of macro variables

IMV = Index of macro variables

b8 = Elasticity coefficient for index of financial variables

IFV = Index of financial variables

Since the methodology is based on a comparison of cri-

ses-hit countries with a non-crises hit country (that is, the

control), this objective can be met only through panel re-

gression. Secondly, panel regression leads to more effi-

cient estimators and the result of the panel regression

supports this objective. Thirdly, the fixed effect model al-

lows measurement of the precise impact of crises on a

differential basis among the crises-ridden countries, and

between the crises-ridden country and control.

Apart from the above-mentioned justification for choos-

ing the fixed effects model in line with the mixed ap-

proach to crises definition, an important basis of choosing

the fixed effects model is that different dummies were

taken with India as a base country. Therefore, the very

basis of this study lies in making a comparison between

India which was relatively unaffected by the crises and

A5 countries which were severely affected. The research

design essentially lends itself to a fixed effects model with

a view to capture discrete country effect on a comparative

basis.

6 This is known as Principal Variable Regression

20

Fixed effects models are not without their drawbacks. They

may frequently have too many cross-sectional units of

observations requiring too many dummy variables for

their specification. Too many dummy variables may sap

the model of sufficient number of degrees of freedom for

adequately powerful statistical tests. Moreover, a model

with many such variables may be plagued with multi-

collinearity, which increases the standard errors and

thereby drains the model of statistical power to test pa-

rameters. If these models contain variables that do not

vary within the groups, parameter estimation may be pre-

cluded. Although the model residuals are assumed to be

normally distributed and homogeneous, there could eas-

ily be country-specific (group wise) heteroscedasticity or

autocorrelation over time that would further plague esti-

mation.

To avoid this kind of problem, only country-specific dum-

mies and one time dummy were used in the final

modeling, as mentioned earlier. Time effect was consid-

ered as fixed. To deal with the problem of multicollinearity,

the index of the impacted/ dependent variables was con-

structed and it was ensured that there was no correlation

among those indexes. The problem of auto-correlation was

taken care of by measuring the Durbin-Watson statistic

which happens to be in the normal range.

RESULT AND ANALYSIS

This section interprets the results of various empirical

procedures which were applied to the data set in order to

arrive at some relevant insights and conclusions.

Causality Test

The Granger causality test consists of testing pairs of equa-

tions expressed below:

Yt = a1 Xt-1 + a2 Xt2 + b1 Yt1 + b2 Yt2 + u1t (6)

Xt = c1 Xt1 + c2 Xt2 + d1 Yt1 + d2 Yt2 + u2 (7)

In Equation 6,

Xt = Independent variable

Yt = Dependent variable

Xt-1 = First lag of independent variable

Xt-2 = Second lag of independent variable

Yt-1 = First lag of dependent variable

Yt-2 = Second lag of dependent variable

a1 = Co-efficient of first lag of independent variable

a2 = Co-efficient of second lag of independent variable

b1 = Co-efficient first lag of dependent variable

b2 = Co-efficient second lag of dependent variable

u1t = Error term of first equation

In Equation 7,

Xt = Dependent variable

Yt = Independent variable

Xt-1 = First lag of dependent variable

Xt-2 = Second lag of dependent variable

Yt-1 = First lag of independent variable

Yt-2 = Second lag of independent variable

c1 = Co-efficient of first lag of dependent variable

c2 = Co-efficient of second lag of dependent variable

d1 = Co-efficient of first lag of independent variable

d2 = Co-efficient of second lag of independent variable

u2t = Error term of second equation

The procedure consists of constructing sets of pairs of

two variables amongst which the causality is being tested

for. Alternatively, each variable acts as the dependent and

independent variable. The lags of both the variables are

included on the swap as either dependent lagged vari-

ables or independent lagged variables. If the lags of the

independent variable significantly impact the depend-

ent variable, then the independent variable is known to

have caused the dependent variable. The procedure is

repeated by swapping the dependent and independent

variable for testing for reverse causality. If the causality is

found in both directions, then it is known as a bi-way

causal relationship ; else it is known as a one-way causal

relationship.

Causality test was conducted on a set of 30 variables. (See

Tables 1 and 2 that describe the causal macro and finan-

cial variables). There were a total of 435 combinations.

Accounting for our own covariance, which was 15 (in

pair) in number, there were 420 combinations. Since the

procedure of testing involved testing in pair, it implies

that 210 causality tests were applied. On account of tran-

sitivity, the number of combination was halved7. The vari-

ables with two-way causality were dropped As in such

cases, one cannot identify which variable is to be taken as

dependent variable and which variable as independent

variable. The result of the causality exercise shows 19

causal variables. On the other hand, there were 16 im-

pacted/dependent variables. (See Tables 3 and 4 that

describe the impacted macro and financial variables).


7 Tested at 10% level of significance. Result not reported


Table 1: Macro Causal Variables (Code and Name of the Variable)

Code Abbreviation of the Variable Name of the Variable

M1 BN.RES.INCL.CD Changes in net reserves (Bop, current US$)

M2 BN.CAB.XOKA.GD.ZS Current account balance (% of GDP)- CAB

M3 NE.EXP.GNFS.ZS Exports of goods and services (% of GDP)-EOGD

M4 NY.GDP.MKTP.KD.ZG GDP growth (annual %)- GDPG

M5 NY.GDP.PCAP.KD.ZG GDP per capita growth (annual %)- GDPPC

M6 NE.GDI.TOTL.ZS Gross capital formation (% of GDP)- GCF

M7 NE.IMP.GNFS.ZS Imports of goods and services (% of GDP)- IOGS

M10 PA.NUS.FCRF Official exchange rate (LCU per US$, period average)- OER % change over previous year

M11 GB.BAL.OVRL.GD.ZS Overall budget balance, including grants (% of GDP)- OBB

M13 FR.INR.RINR Real interest rate (%)- RI

Table 2: Financial Causal Variables (Code and Name of the Variable)


F2 FS.AST.PRVT.GD.ZS Domestic credit to private sector (% of GDP)- DCTPS

F3 GB.FIN.DOMS.GD.ZS Domestic financing, total (% of GDP)- DFT

F4 BX.KLT.DINV.DT.GI.ZS Foreign direct investment, net inflows (% of gross capital formation)- FDI

F6 IQ.ICR.RISK.XQ ICRG composite risk rating (0=highest risk to 100=lowest)- CRR

F8 FR.INR.LEND Lending interest rate (%)- LR

F10 CM.MKT.LCAP.GD.ZS Market capitalization of listed companies (% of GDP)- MC

F12 DT.DOD.DSTC.ZS Short-term debt (% of total external debt)- STD

F13 CM.MKT.TRAD.GD.ZS Stocks traded, total value (% of GDP)- ST

F15 DT.TDS.DECT.GN.ZS Total debt service (% of GNI)- TDS GNI

Table 3: Impacted Macro Variables (Code and Name of the Variable)


M1C BN.RES.INCL.CD Changes in net reserves (BoP, current US$)- CINR

M2C BN.CAB.XOKA.GD.ZS Current account balance (% of GDP)- CAB

M3C NE.EXP.GNFS.ZS Exports of goods and services (% of GDP)- EOGD

M5C NY.GDP.PCAP.KD.ZG GDP per capita growth (annual %)- GDPPC

M7C NE.IMP.GNFS.ZS Imports of goods and services (% of GDP)- IOGS

M10C PA.NUS.FCRF Official exchange rate (LCU per US$, period average)- OER % change over previous year

M13C FR.INR.RINR Real interest rate (%)- RI

M9 FP.CPI.TOTL.ZG Inflation, consumer prices (annual %)

Table 4: Impacted Financial Variables (Code and Name of the Variable)


F2C FS.AST.PRVT.GD.ZS Domestic credit to private sector (% of GDP)- DCTPS

F4C BX.KLT.DINV.DT.GI.ZS Foreign direct investment, net inflows (% of gross capital formation)- FDI

F5C BG.KAC.FNEI.GD.ZS Gross private capital flows (% of GDP)- GCF

F6C IQ.ICR.RISK.XQ ICRG composite risk rating (0=highest risk to 100=lowest)- CRR

F8C FR.INR.LEND Lending interest rate (%)- LR

F12C DT.DOD.DSTC.ZS Short-term debt (% of total external debt)- STD

F13C CM.MKT.TRAD.GD.ZS Stocks traded, total value (% of GDP)- ST

F15C DT.TDS.DECT.GN.ZS Total debt service (% of GNI)- TDS GNI

22

The common variables which occurred both as a cause

and impact variable are shown in Table 58. In all, there

were 15 common variables. Thus out of 16 impacted vari-

ables, only one variable was left as pure impacted vari-

able, that is, M9 (FP.CPI.TOTL.ZGInflation, consumer

prices (annual %)).

Correlation Analysis

Out of the 15 common variables, which ones would be

retained as impacted variables and form a part of the in-

dex of crisis was sorted out through correlation analysis.

First, correlation among the pure impacted variable M 9,

inflation, consumer prices (annual %) and the 15 com-

mon variables was calculated9. Out of the list of 15 com-

mon variables, only 14 were retained. Variable F15 - Total

debt service (% of GNI) was dropped because of non-avail-

ability of data in case of some countries. The list of 15

common variables are given in Table 5.

Common variables that were correlated with the single

impacted variable M9 inflation, consumer prices (annual

%) were retained as impacted/dependent variable. One

can argue that correlation could be high in case of im-

pacted variable, since a composite index was constructed

out of it with the help of PCA that ensured that the corre-

lation was removed. After applying correlation analysis,

8 Marked as C in Table 5.9 By using SPSS 15

the variables which were found to be highly correlated

with the pure impacted variables have been reported in

Table 6.

Formation of Index of Crises

PCA was applied on impacted variables. The final proce-

dure for the formation of the index involved the following

steps:

1. Determination of number of principal components to

be retained. For this, the Kaiser criteria was used and

three principal components where eigen value was

greater than one, were retained. Table 7(a) shows the

total variance explained by the extracted principal

component. It is evident that over 72 percent of the

information is captured by the retained component.

2. Rotation of components: With the help of Varimax ro-

tation with Kaiser normalization, the components were

rotated. This was done with a view to obtain the clear

interpretation of the components. This resulted in a

set of component scores for each of the nine variables

with respect to the three retained components. Table

7(b) reports the component scores coefficient matrix.

3. Selection of principal variables: The Joliffe procedure

(explained earlier) was used to select the principal

variables. Three variables were selected in the de-


Table 5: List of Common Variables


M1C BN.RES.INCL.CD Changes in net reserves (BoP, current US$)- CINR

M2C BN.CAB.XOKA.GD.ZS Current account balance (% of GDP)- CAB

M3C NE.EXP.GNFS.ZS Exports of goods and services (% of GDP)- EOGD

M5C NY.GDP.PCAP.KD.ZG GDP per capita growth (annual %)- GDPPC

M7C NE.IMP.GNFS.ZS Imports of goods and services (% of GDP)- IOGS

M10C PA.NUS.FCRF Official exchange rate (LCU per US$, period average)- OER % change over previous year

M13C FR.INR.RINR Real interest rate (%)- RI

F2C FS.AST.PRVT.GD.ZS Domestic credit to private sector (% of GDP)- DCTPS

F4C BX.KLT.DINV.DT.GI.ZS Foreign direct investment, net inflows (% of gross capital formation)- FDI

F5C BG.KAC.FNEI.GD.ZS Gross private capital flows (% of GDP)- GCF

F6C IQ.ICR.RISK.XQ ICRG composite risk rating (0=highest risk to 100=lowest)- CRR

F8C FR.INR.LEND Lending interest rate (%)- LR

F12C DT.DOD.DSTC.ZS Short-term debt (% of total external debt)- STD

F13C CM.MKT.TRAD.GD.ZS Stocks traded, total value (% of GDP)- ST

*F15C DT.TDS.DECT.GN.ZS Total debt service (% of GNI)- TDS GNI

* Variable which was dropped due to non-availability of data in case of some countries.


Table 6: Variables Significantly Correlated with Variable M9


M13 FR.INR.RINR Real interest rate (%)

M1 BN.RES.INCL.CD Changes in net reserves (Bop, current US$)

M3 NE.EXP.GNFS.ZS Exports of goods and services (% of GDP)

M2 BN.CAB.XOKA.GD.ZS Current account balance (% of GDP)

M7 NE.IMP.GNFS.ZS Imports of goods and services (% of GDP)

M10 PA.NUS.FCRF Official exchange rate (LCU per US$, period average) % change over previous year

F2 FS.AST.PRVT.GD.ZS Domestic credit to private sector (% of GDP)

F8 FR.INR.LEND Lending interest rate (%)

*M9 FP.CPI.TOTL.ZG Inflation, consumer prices (annual %)

Table 7(a): Total Variance Explained

Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings

Component Total % of Variance Cumulative % Total % of Variance Cumulative %

1 3.143 34.921 34.921 3.013 33.482 33.482

2 2.352 26.137 61.058 2.300 25.553 59.035

3 1.052 11.687 72.745 1.234 13.709 72.745

Extraction Method: Principal Component Analysis

Table 7(b): Component Score Coefficient Matrix

Component

1 2 3

CINRM1 -0.055 -0.072 0.366

CABM2 0.236 -0.002 0.485

EOGDM3 0.315 0.022 -0.019

IOGSM7 0.297 0.021 -0.062

ICPM9 -0.098 0.361 0.048

OERM10 0.009 0.402 -0.030

RIM13 -0.053 -0.384 0.147

DCTPSF2 0.299 -0.051 0.104

LRF8 0.069 -0.044 0.694

Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization

Table 7(c): Correlations

EOGDM3 OERM10 LRF8

EOGDM3 Pearson Correlation 1 0.048 -0.268*

Sig. (2-tailed) . 0.654 0.011

N 90 90 90

OERM10 Pearson Correlation 0.048 1 0.094

Sig. (2-tailed) 0.654 . 0.379

N 90 90 90

LRF8 Pearson Correlation -0.268* 0.094 1

Sig. (2-tailed) 0.011 0.379 .

N 90 90 90

* Correlation is significant at the 0.05 level (2-tailed).

24

scending order beginning with the largest component.

Accordingly, the three principal variables selected

were; M 3 Exports of Goods and Services (% of GDP),

M 10 Official Exchange Rate (LCU per US$, period

average, % change over the previous year), and F 8

Lending Interest Rate (%).

4. Construction of index of dependent variable. By using

the weights from the component score coefficient ma-

trix (given in step 3 of the PCA analysis), the index of

dependent variables was constructed. The composite

index of impacted variables (Y variable/the LHS vari-

able) was calculated by multiplying the variables with

their respective weights.

(8)

The code of the variable refers to the value and direction

of each included variable in relation to the value and di-

rection of the index. IOC measures the crises; therefore, a

higher value of the index should represent a higher de-

gree of crises. Percentage change in the official exchange

rate over the previous year10 is expressed as LCU/$; there-

fore, a rise in its value would represent depreciation of

domestic currency. In effect, a higher value implies an

increase in the degree of crises. With depreciation, it may

be expected that value of exports of goods and services as

a percentage of GDP would increase which would also

add to the value of the crises index. Similarly, a higher

lending interest rate could also be expected during the

period of crises. Thus, all the three variables conform to

the desired code of IOC. That is, they rise in value term

when the crises increase; so also does the index of crises.

Thus, the code of the components of the index and the

crises index share the same interpretation.

During the crises, in general, there would be a tendency

of inflation to rise. Secondly, there could be a speculative

bubble; therefore, after the monetary authority resorts to

tight money policy, the interest rate is likely to increase.

There was a dual purpose for adopting this elaborate pro-

cedure.

Firstly, it was aimed at developing a composite index.

Secondly, it was important to ensure that a correlation

amongst retained variables was minimized which is a

merit of PCA methodology. After having constructed an

index, it was necessary to verify the degree of correlation.

Table 7(c) shows that the correlations amongst the

retained principal variables that have been used for con-

structing an index were low and not statistically signifi-

cant.

Index of Macro Variables

In the second stage of creating of composite index, the

index of causal macro variable was calculated.

We had a combined list of 10 causal variables, for both

financial and macro variables (Table 8).

Table 8: Combined List of Causal Variables

Code Abbreviation of Name of thethe Variable Variable

M4 NY.GDP.MKTP.KD.ZG GDP growth (annual %)

M6 NE.GDI.TOTL.ZS Gross capital formation(% of GDP)

M11 GB.BAL.OVRL.GD.ZS Overall budgetbalance, includinggrants (% of GDP)

F3 GB.FIN.DOMS.GD.ZS Domestic financing,total (% of GDP)

F10 CM.MKT.LCAP.GD.ZS Market capitalization oflisted companies (% ofGDP)

M5 NY.GDP.PCAP.KD.ZG GDP per capita growth(annual %)

F13 CM.MKT.TRAD.GD.ZS Stocks traded, totalvalue (% of GDP)

F4 BX.KLT.DINV.DT.GI.ZS Foreign direct invest-ment, net inflows (% ofgross capital formation)

F6 IQ.ICR.RISK.XQ ICRG composite riskrating (0=highest risk to100=lowest)

F12 DT.DOD.DSTC.ZS Short-term debt (% oftotal external debt)

In the first place, out of the four macro variables, GDP per

capita growth was dropped on account of data redun-

dancy. To make a composite index, PCA was applied on

the list of macro causal variables. The results of PCA are

shown in Tables 9(a) (c). The Kaiser criterion was ap-

plied for choosing components whose eigen value was

greater than one. Accordingly, three components were

retained and the total variance explained by these com-

ponents was 100.10 Since we had to take % change over previous year for normaliza-

tion of the variable, we had to forego one data point, that is, 1987.The final period therefore is 1988 to 2002.



Table 9(b) shows the rotated component score coefficient

of the three macro independent variables that were being

sought to collapse into an index. The coefficients show

the factor loading of these three variables with respect to

the three retained principal components. Since all the

three principal variables were being retained, the compo-

nent score matrix was used to identify the weights of each

of these variables. Joliffe procedure (explained earlier) was

used to select the principal variables.

(9)

The index of macro variables was calculated as the value

of the variables multiplied by its weights which were re-

ported in the component score coefficient matrix. The se-

lected variables are, M4 NY.GDP.MKTP.KD.ZGGDP

growth (annual %), M6 NE.GDI.TOTL.ZSGross capi-

tal formation (% of GDP), and variable M11 GB.BAL.

OVRL.GD.ZS Overall budget balance, including grants

(% of GDP).

The final step in the procedure was to examine the corre-

lation matrix because the study was dealing with inter-

correlated variables that were sorted through a long proc-

ess including causality tests. Presented in Table 9(c) is

the correlation matrix of the components of the index of

macro variables. M11 and M4 seem to have a high corre-

lation, but as it is statistically not significant, it would not

cause mulicollinearity of any serious order.

The code of the variable refers to the interpretation of the

value of the direction of change with respect to the value

of the measure of the index of macro variables. As the

growth rate of GDP and the Gross Capital Formation de-

cline, the crises can be expected to increase. When the

overall budget balance (OBB) increases under conditions

of deficit budget, OBB would fall further and would lead

to a crisis. Hence, a negative relationship among the vari-

ables similar to the macro variable index was expected.

Index of Financial Variables

In the third stage of creating of composite index, the in-

dex of causal financial variable was calculated. To make

a composite index, PCA was applied on the list of finan-

cial causal variables. The results of PCA are given in Ta-

bles 10 (a)-(c).


Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings

Component Total % of Variance Cumulative % Total % of Variance Cumulative %

1 2.156 71.869 71.869 1.186 39.531 39.531

2 0.700 23.339 95.207 1.048 34.919 74.450

3 0.144 4.793 100.00 0.766 25.550 100.00

Extraction Method: Principal Component Analysis


Component

1 2 3

GDPGM4 -0.684 -0.254 1.873

GCFM6 -0.036 1.108 -0.380

OBBM11 1.507 -0.024 -1.079

Extraction Method: Principal Component Analysis; Rotation Method: Varimax with Kaiser Normalization

Table 9(c): Correlation Matrix

EOGDM3 OERM10 LRF8

Correlation GDPGM4 1.000 0.504 0.838

GCFM6 0.504 1.000 0.351

OBBM11 0.838 0.351 1.000

26


Component Initial Extraction Sums of Rotation Sums ofEigen Values Squared Loadings Squared Loadings

Total % of Cumulative Total % of Cumulative Total % of CumulativeVariance % Variance % Variance %

1 1.828 45.706 45.706 1.828 45.706 45.706 1.535 38.384 38.384

2 1.101 27.514 73.220 1.101 27.514 73.220 1.203 30.082 68.465

3 0.817 20.429 93.650 0.817 20.429 93.650 1.007 25.184 93.650

4 0.254 6.350 100.00

Extraction Method: Principal Component Analysis.


Component

1 2 3

F3 -0.047 0.122 1.025

F6_MOD 0.678 0.274 -0.014

F13 0.144 0.864 0.092

F10 -0.460 0.294 0.056

Extraction Method: Principal Component Analysis; Rotation Method: Varimax with Kaiser Normalization

Table 10(c): Correlation Matrix

F3 F6_MOD F13 F10

Correlation F3 1.000 0.070 -0.224 -0.198

F6_MOD 0.070 1.000 0.011 -0.596

F13 -0.224 0.011 1.000 0.440

F10 -0.198 -0.596 0.440 1.000

The criterion for choosing three components was applied.

By the Kaiser criterion, the coverage was not adequate

(68.465). Accordingly, three components were retained

and the total variance explained by these components

was 93.650.

Table 10(b) shows the rotated component score coefficient

of the four financial independent variables that were be-

ing sought to collapse into an index. The coefficients show

the factor loading of these four variables with respect to

the three retained principal component. The Joliffe crite-

rion was used to retain the three principal variables. The

component score matrix identifies the weights of each of

these variables.

(10)

The index of financial variables was calculated as the

value of the variables multiplied by its weights which

were reported in the component score coefficient matrix.

The three retained variables were: F3 - GB.FIN.DOMS.GD,

ZSDomestic financing (% of GDP), F6 - IQ.ICR.RISK.

XQICRG composite risk rating (0=higher risk to

100=lowest risk) and F13 - CM.MKT.TRAD.GD.ZS

Stocks traded, total value (% of GDP). These were multi-

plied by the respective scores.

As in the case of macro variables, the final step in the

procedure was to examine the correlation matrix because

the study was dealing with inter-correlated variables that

were sorted through a long process including causality

test. Table 10(c) presents the correlation matrix of the com-

ponent of the index of financial variables. There was some

moderate correlation between F10 and F6_MOD; how-

ever, none of the correlation was statistically significant.

Therefore, there was no problem of mulicollinearity.



The code of the variable refers to the interpretation of the

value of the direction of change with respect to the value

of the measure of the index of financial variable. The total

value of stocks traded is an indicator of the capital mar-

ket activity. The total value would be influenced by the

turnover as well as the value of assets traded. When the

markets are on a high, both these aspects are likely to be

high. There may be a high degree of overvaluation of

shares. During a crisis, the turnover may also be high.

And, if the fundamentals of the economy are not strong,

such an excessive bubble could lead to crises. Therefore,

it may be expected that an increase in the value of stocks

traded would promote crises.

Domestic financing to private sector is to be interpreted

as the availability of excess credit. In the event that this

credit is feeding the asset bubble, it would result in greater

volatility and an inflation of asset prices that is unrelated

to the fundamental of the economy. This would therefore

be a major influence in precipitating crises.

The third variable which was included in the index of

financial variable was (F6) ICRG - composite risk rating

(0=highest risk to 100=lowest risk). The code of this vari-

able was running contrary to the direction of the index

because a lower value represents a higher risk. The final

form of this variable, which was a risk indicator, had to

bear a positive relationship with the index of crises. There-

fore, the variable was modified by defining it as the com-

plement of the original variable F6. This was achieved by

subtracting the value of each data point from 100. We

tested the modified F6 variable by applying PCA and

found that the result did not change either in terms of

variable selection form principal component or as com-

ponent scores. The advantage therefore is that the index

of financial variable would now consist of all the three

variables that move in the same direction. Thus, a higher

value of the index clearly defines a greater level of the

factors that belong to the financial index. In this sense,

when the modified F6 is larger, the risk would be larger

and hence the potential of crises is larger. It may also be

recalled that higher risk implies greater volatility which

is the single most important factor responsible for crises.

On the basis of the above consideration, we expect that

the index of financial variables would have a positive

relationship with the index of crises.

MODEL SPECIFICATION AND ESTIMATION

Once it was clear which variable was to be taken as a

dependent variable and which other variables were to be

taken as independent variable, the choice was between

the appropriate model, functional form, and estimation

technique.

As far as model specification is concerned, crisis was con-

sidered as a function of 16 macro variables, 14 financial

variables, and a set of exogenous variables. Linear func-

tional form was adopted for the model. The log linear

form could not be taken as some data points were nega-

tive and some were in percentage terms.

A model was constructed which included n-1 countries

dummies and only one time dummy which represented

the crises period. Difference dummy was used for the in-

tercept dummy in the final model. Although there were

three periods (pre-crises, crises, or postcrises), only one

time dummy was used in the final model instead of two

because the study was interested only in the crises pe-

riod. More degree of freedom was desirable. Such a model

is represented by equation 11:

LIOCt = A +b1 * (CW) t + b2d2 + b3d3 + b4d4 +

b5d5 + b6d6 +b7 * LIDXFVit + b8* LIDXMVit +U1it (11)

IOCt = Dependent variable = Log (Index of Crisis)

A = intercept

b1= Coefficient of crisis window

b2 = Intercept dummy for Thailand

b3 = Intercept dummy for Philippines

b4 = Intercept dummy for Korea Republic

b5 = Intercept dummy for Indonesia

b6 = Intercept dummy for Malaysia

(CW)t = Dummy for crisis window: 0 for all years & 1

for 1997, 1998

b7 = Elasticity coefficient for index of macro variables

IMV = Index of macro variables

b8 = Elasticity coefficient for index of financial variables

IFV = Index of financial variables

U1it = Error term

i = 1 to 6 by country

t = 1988 to 2002

Determinants of Crises

The main purpose of the foregoing analyses, discussion

on methods, and model specification is to estimate a struc-

tured model which seeks to explain currency crises along

28

Table 11: Final Model

Regression Statistics

Multiple R 0.85231

R Square 0.726433

Adjusted R Square 0.699414

Standard Error 0.229639

Observations 90

ANOVA

df SS MS F Significance F

Regression 8 11.34244 1.417805 26.88602 8.00433E-20

Residual 81 4.271447 0.052734

Total 89 15.61389

Coefficients Standard Error t Stat P-value

Intercept 2.872204 0.568448 5.052712 2.64E-06 17.67593008

CW 0.362984 0.073842 4.915711 4.55E-06 1.437612841

D2 0.516381 0.096763 5.336526 8.43E-07 1.675950899

D3 0.406131 0.084158 4.825837 6.46E-06 1.500999912

D4 1.374522 0.177197 7.757031 2.26E-11 3.953185275

D5 0.549734 0.085405 6.436757 8.05E-09 1.732792008

D6 0.980138 0.103953 9.42863 1.13E-14 2.664823967

LIDXFV 0.209432 0.124327 1.684531 0.095929 1.232977862

LIDXMV -0.25518 0.07065 -3.61194 0.000526 0.774774645

with individual differences among countries and other

fixed effects through a set of explanatory variables. These

explanatory variables are the determinants of crises in

the three policy periods provided that their parameters

are found to be statistically significant. For this purpose,

the following fixed effects panel regression was estimated.

The results are reported in Table 11.

The result of Equation 11, the final model is reported as

follows:

LIOCit = A + 0.36 (CW) t + 0.51 (d2) + 0.41

(d3) + 1.37 (d4) + 0.54 (d5) + 0.98 (d6) + -0.20 *

LIDXFVit + (0.26) * LIDXMVit + U1it (12) (12)

In the estimated double log model, financial variables

index is significant at 10 percent which is an acceptable

threshold. This is presumably so due to higher volatility

of financial variables; therefore, standard error is high.

So far, as a determinant, the impact of financial variables

on the crisis variables is less definite in comparison to the

macro index. The value of R square is fairly high at 0.72.

However, in general, the index of financial variables has

a smaller elasticity of 0.21 approximately, but it is posi-

tive which indicates that according to our continuous

definition of crises, financial variables are responsible

for escalating the crisis index. The positive signs clearly

show that financial variables are responsible for enhanc-

ing the crises on a continuous basis.

The index of macroeconomic variables, on the other hand,

has slightly larger magnitude of 0.255. However, macro-

economic variables index bears a negative sign. This im-

plies that macro variables in general reduce the magnitude

of crises. The macro variables therefore stabilize the

economy and hence reduce the intensity of crisis.

Yet, it needs to be pointed out that both the indices have

elasticities that are less than one. This has a significant

bearing on our measurement and analysis of crises. In

terms of magnitude, the intercept is large and significant.

This implies that there is a large domain of unknown

variables which have been omitted whose influences on

crises are substantial and significant. This means that

our understanding and measurement of crises has a large

element of uncertainty. It must be remembered that the

intercept contributes to the determination of the predicted

variable-index of crisis.



Secondly, the country differentials such as culture, policy,

historical circumstances, etc. are all captured by the coun-

try dummy. Since all these are significant and positive, it

implies that country differential plays an important role

in determining crisis. It is difficult to further segregate

these effects that are country-specific. However, the meth-

odology adopted in the paper takes into account the in-

formation content in the large set of variables. It is thus

most comprehensive in terms of its explanatory power

and its potential for measurement.

There were various models for the estimation of panel

data. This study used fixed effects approach in which the

slope coefficients were constant but the intercept varied

across countries, that is, fixed effects or least square

dummy variable (LSDV) regression model. The fixed ef-

fects arise due to the special features of different coun-

tries such as economic structure, political condition,

conditions of macro and financial fundamentals, coun-

tries linkages with the rest of the world in terms of the

trade, capital market, and money market linkages. The

term fixed effect denotes that although the intercept may

differ across countries (six in the present study), each in-

dividual countrys intercept does not vary over time; that

is, it is time invariant. It may be noted that fixed effect

model assumes that the slope coefficient of the independ-

ent variables such as index of macro and financial vari-

ables do not vary across countries or over time.

D2 =1, if the observation belongs to Thailand, 0, other-

wise and so on for other countries, seriatum. Since the

study considered six countries, only five dummies were

used to avoid falling into the dummy variable trap (that

is, the situation of perfect co-linearity). There was no

dummy for India. In other words, the overall intercept (A)

represents the intercept of India and A+d2 represents the

differential intercept coefficient of Thailand and so on. It

tells by how much the intercept of the rest of the countries

differ from the intercept of India, since India is the base

country. Time effect was not included in the model. Hence,

the crises function does not shift over time. There is only

one dummy to represent the crisis window which takes

the value 0 for all years except 1997 and 1998 where it

takes the value 1.

One of the main findings of the estimation confirms our

conception of crises and vindicates our understanding

that the crises definition needs to be built into the causal

variables. Accordingly, we had introduced a dummy vari-

able to capture a discrete change in the index of crises.

This variable was termed as CW. It basically implies a

discrete jump in the index of crises during the period of

crises. Therefore, while the continuous explanatory vari-

able explains the buildup to the crises, it is the set of dis-

crete intercept dummies and the CW variable that capture

the discrete aspects of crises. The country dummies en-

able international comparison while CW captures inter-

temporal comparison. It is therefore clear that the crises

phenomenon is a combination of continuous as well as

discrete change. The continuous effect arises on account

of a set of variables incorporated in the two indices. The

speciality of our approach is that it captures the genesis

of crises which is the volatility in the causal and depend-

ent variables. This has been achieved by constructing the

variables through PCA methodology which maximizes

the variance. Therefore, the interpretation of the beta coef-

ficients of the continuous variables is that it shows the

relationship between the volatility of causal variables and

the effects on the dependent variable in the form of vola-

tility in the crises variables.

The crises window was highly significant; hence the defi-

nition of the crises window is correct as it captures the

crises as is clear from the p value. All the coefficients are

significant.

As far as the individual countrys shocks are concerned,

that may be due to omitted variables which may be im-

portant in respect of that country. For example, in case of

Korea, withdrawal of short-term debt was one of the ma-

jor problems. Likewise in case of Thailand, depletion of

foreign reserves was the major problem.

Korea was most affected by the crises in this analysis as itis clear from the intercept of D4 which is 21.629. It turns

out to be an economy that has undergone severe shock.

The p value in case of Korea was very low; it means that

the level of significance was high. It could be due to the

size of the economy among all the A5 countries, it was

Korea which was giant in size. In fact, it was the worlds

11th largest economy in terms of GDP at that time. An-

other possible explanation of this might be that all the

crises-hit countries, except Korea, had abounded their

earlier monetary system; if they were on the peg system

like Thailand, they would have announced a managed

float, like the Philippines which allows its peso to move

in a wider range against the dollar and Indonesia which

allows its rupiah to float. In November of 1997, it was

30

only Korea which was on the existing exchange rate sys-

tem. Moreover, by that time all the rest of the countries

had approached the International Monetary Fund for tech-

nical as well as financial assistance or International Mon-

etary Fund itself had offered a financial support package

to the crises-hit countries. For instance, in July 1997, IMF

had offered $1.1 billion as financial support to the

Phillipines. IMF had also approved $16.7 billion credit

for Thailand. Korea had not approached it until the first

week of December 1997. It tried to handle the problem on

its own, because the financial assistance from IMF was

coming with a cost of rising interest rate. The Korean

economy could not have sustained it since it was already

affected by the banking crises in January of 1997. Hanbo

Steel, a large Korean Chaebol, collapsed under a $6 bil-

lion debt load; it was the first Korean conglomerate in a

decade. In November, the sharp rise of dollar against the

Japaneses yen in global trade boosted the currency against

won in Korea. In South Korea, sentiments were negative;

media report in the Western press also stated that the

South Korean economic crisis was set to get worse. The

Korean government defended itself against the foreign

press report which suggested that the countrys financial

turmoil could surpass the recent market meltdowns in

Thailand, Indonesia, and the Philippines. The reports in

major international publication also reported that South

Koreas foreign reserves were dwindling and that the

country might need assistance from the IMF.

South Koreas woes began when its trade deficits bal-

looned in the year 1996 and the value of its currency

slipped after the South East Asian market shock waves

reached the country. That came at the time when the mar-

ket was saddled with billions of dollar of bad loans in the

wake of a series of major bankruptcies. The international

rating agencies downgraded Koreas foreign debt. Inter-

est rates soared and the stock market plummeted 28 per-

cent in the year 1997 to reach a five-year low at the end of

October 1997. South Korea was the worlds 11th largest

economy larger than Thailand, Indonesia, and Malaysia

put together and the prospects of the financial crises had

put everyone from the Japanese exporters to investors in

Latin America on edge. Over 50 percent of the Korean

exports compete directly with Japanese products. The

wons move against the dollar often mirrors the yen trad-

ing movements versus the US currency. As local export-

ers tried to make their products more attractively priced

compared with the products of their Japanese rivals, any

positive effect of wons depreciation was effectively can-

celled out by the yen decline vis--vis the US dollar. Ko-

rea in its own way was right in not abandoning its current

system; for doing this, they had to completely liberalize

the market. It was too early to do so in view of their eco-

nomic status and in view of the fact that won was not

fully convertible. By mid-November 1997, the uncertainty

surrounding Korea had pressurized the regional curren-

cies, the hardest hit being the Thai baht, the Philippine

peso, the Malaysian ringgit, and the Indonesian rupiah.

According to one view, the Korean currency turmoil was

unlikely to stop at its own borders. If the won depreci-

ated, the yen was likely to depreciate as well.

An IMF bailout was a humiliating necessity for South

Korea, proud of its meteoric rise from a war-torn nation to

the worlds 11th largest economic powerhouse. IMF bail-

out required stringent economic reforms and policy con-

trols. We can say that South Korea was the latest wounded

tiger to suffer heavy losses, its economic plight and de-

lays in signing a loan agreement with the IMF being the

reasons for the same.

In the case of Indonesia, the intercept was higher (19.408)

than the base country (India 17.67). The p value was also

very low implying that the level of significance was high.

Indonesia abolished its system of managing the exchange

rate through the use of band and allowed it to float. IMF

gave Indonesia $23 billion financial support package. The

second most affected country by our analysis was Malay-

sia whose p value was quite low. It is also clear from the

intercept value of D6. The Malaysian Central Bank re-

stricted loans to property and stocks to head off a crisis.

In case of Thailand, the intercept was higher than the

base country. The Thai baht was hit by the attack by specu-

lators who considered Thailands slowing economy and

political instability as the right time to sell. The move to

save Finance One, Thailand largest finance company,

failed. The Thai Central Bank suspended operations of

16 cash-strapped finance companies and ordered them

to submit merger or consolidation plan. On July 2 1997,

the Bank of Thailand announced a managed float of the

baht and called IMF for technical assistance. The an-

nouncement effectively devalued the baht by about 15-20

percent. This was triggered by the East Asian Crises. Our

analysis clearly reveals that it was not most hit by the

crises. In case of the Philippines, the intercept was higher

than the base country indicating that although it was

affected by the crises, the impact was not severe. The cen-



tral bank raised the overnight rate by 1.34 percent to 13

percent and again to 24 percent thus dumping the dollar.

The Philippines Central Bank allowed the peso to move

in a wider range against the dollar. The IMF backed the

move and the board approved the Extended Fund Facil-

ity. Hence the effect was not distinct. It was low and un-

certain.

In summary, the main purpose of the regression was to

identify a set of dependent variables that formed the in-

dex of crises and a set of independent variables which

formed the index of financial variables as well as the in-

dex of macro variables. Table 12 summarizes the cause

and effect relationship that explains the crises. The Table

does not indicate the countries dummies and the time

dummy.11

From the main model, the predicted value of the index of

crises was estimated. The first observation is that during

the crises window, all countries, including India, have

been clearly affected. There is evidence of a discrete jump

in the predicted index across countries. However, it can

be seen that the impact on India was the minimal. The

highest index was that of Indonesia which stood at

60.83157, while India, with 23, was the lowest. Most of

the countries during the crises were in the range of 60s.

The maximum rise was in the case of Indonesia that was

around 24 points. In Korea, on an average, the jump was

just about one point. Similarly, in the case of Thailand

and the Philippines, the appreciation was 11 and 7 points

respectively.

During the recovery phase, the patterns were more stable.

In the case of India, there was a decline down to 40 per-

cent and the recovery was almost complete except for a

marginal overall rise in comparison to the pre-crises pe-

riod. Both the Philippines and Thailand experienced a

halving of the index after crises and a mild decline to-

wards pre-crises levels in the next three years. In the case

of Korea, while the dip in the index was down by one

third, there was a marginal rise and a stable trend which

resembled that of the late 1980s. In both Malaysia and

Indonesia, the decline was less than half and there was a

mild tendency towards a falling index which approxi-

mated their state at the end of 1980s and beginning of

1990s (Table 12). This pattern is depicted in Figure 1.

CONCLUDING REMARKS

The first objective of this study was to identify the causal

and impacted variables and the second was to sort them

into LHS and RHS variables for which correlation analy-

sis was used. Both the objectives were duly met. The third

objective i.e. to form indices was met through PCA

11 We also tested the model in which we first considered the interaction between time and crises window. We also tested the equation in whichwe considered individuals variables and not the indexes. The results were not significant.

Table 12: Predicted Index of Crisis

Year Thailand Philippines Korea Indonesia Malaysia India

1988 21.55736 25.96764 41.03978 27.37488 38.32451 16.36509

1989 20.6508 25.18948 40.64091 25.83559 36.86196 17.32047

1990 20.30497 26.81612 41.77969 25.84149 33.37887 17.91071

1991 20.58842 29.20521 42.27586 23.13533 32.34299 19.33262

1992 20.54555 25.74871 42.23782 23.5756 32.17903 16.69407

1993 20.20048 23.58768 42.29674 23.35578 30.22425 16.90031

1994 20.09617 21.84762 42.81112 23.01697 29.74991 15.10235

1995 19.43599 22.28141 42.6515 22.59393 29.65968 14.57693

1996 19.2316 21.15191 41.89849 23.00288 29.59737 15.15495

1997 37.09209 30.72588 60.01316 36.47265 46.58788 23.03583

1998 40.58957 37.11995 60.62232 60.83157 66.57738 23.11819

1999 29.18055 24.66319 41.00381 36.3185 36.59223 15.51987

2000 24.4165 23.74109 41.58963 30.83911 34.28066 16.73032

2001 25.44351 24.47137 41.43097 30.0384 39.19026 15.98588

2002 23.65691 24.39335 41.4666 30.89146 36.50731 15.9514

32 MEASUREMENT OF INTERNATIONAL CURRENCY CRISES: A PANEL DATA APPROACH ...

data reduction and weighting procedure. This was the

crux of the methodology. The indices captured the vola-

tility of the underlying variables, successfully summariz-

ing the information of the macro and financial causal

variables, while the model captured the effect of these

causal variables on the crises phenomenon. The next ob-

jective was to specify a model which reflected various

aspects of the problem, namely crises definition (continu-

ous and discrete), crises window, individual country ef-

fect, inter-temporal and international comparison with

respect to the benchmark country and better estimators.

This was achieved with the help of a panel data fixed

effects model in difference form. This confirms our notion

of the crises phenomenon as follows:

The crises develops continuously on account of vola-

tility of variables.

The crises period can be captured and explained with

the help of certain discrete effects.

The crises can be understood with reference to a base

country.

A set of variables, both financial and macro, acts as a

composite index and determines crises in a continu-

ous way.

Various other models which were tried out only reaffirmed

our conceptualization and methods of measurement of

crises because those models could not perform as well as

our chosen model. In the process, the problem of multi-

collinearity could be overcome, although the effect of a

large number of interrelated variables was incorporated.

It has been found that the index of crises consists of ex-

ports, exchange rate, and interest rate. The financial in-

dex contains risk rating, domestic financing, and stock

traded. The index of macro variables is based on GDP,

capital formation, and budget balance. Macro index nega-

tively influences crisis and financial index influences cri-

sis positively. The decomposition effects show that the

major influence is that of financial variables amongst

which stock traded and domestic financing are the most

important causes of crisis. Amongst the macro variables,

GDP growth is the major influence.

The main contribution of this paper lies in developing an

appropriate methodology that is capable of explaining

such a complex phenomenon as crisis in terms of two

indices of macro-economic and financial variables. In

this methodological paper, India is treated as the base

country. This shows that this methodology is capable of

making a comparative analysis between those countries

that are directly affected by crisis and, those like India,

which are indirectly affected. In the present context, this

means that a similar study can be done by taking India as

a base and comparing it with some of the developed econo-

Figure 1: Predicted Indices of Crisis

Prediction of Crisis

Thailand

Philippines

Korea

Indonesia

Malaysia

India

70

60

50

40

30

20

10

0

IOC

19

88

19

89

19

90

19

91

19

92

19

93

19

94

19

95

19

96

19

97

19

98

19

99

20

00

20

01

20

02


mies like US, the UK, and the EU. Secondly, the PCA along

with Granger causality would help in identifying causal

and impacted variables. Also, the methodology for com-

posite index formation can be applied for summarizing a

larger number of macro-economic and financial variables

which were affected during the global financial crises.

The concept of innovatively combining discrete and con-

tinuous crises definitions in this paper can also be ap-

plied successfully to the current international crises.

Hence, this paper can easily be extended to the present

financial crises.

Appendix: Summary of Literature Review

Study Kaminsky, Lizondo, & Reinhart (KLR) (1998)

Dataset Monthly data for 20 countries, 1970-1995 (Berg & Pattillo or BP, 1999)

Crisis Definition Weighted average of ER changes and reserve changes; threshold is mean +3* standard deviation; separatetreatment for high inflation countries.

Exclusion Window None

Indicators 15 indicators capturing external balance, monetary factors, output and equity movements

Approach Signals set thresholds for indicators to minimize noise-to-signal ratio

Results RER (deviation from deterministic trend), M2/reserves excess M1 growth, reserves, exports, domesticcredit, M2 multiplier (all % changes) good predictors

Asian Crisis Results None

Notes BP (1999) suggest using level of M2/reserves

Study BP (1999) Frankel & Rose (1996)

Dataset KLR (1998); 5 European economies, + 8 emerging Annual data from 1971-1992 for 105 countriesmarkets; 1970-April 1995 (24months beforeThai crisis)

Crisis Definition Same as KLR (1998) ER change > 25% and which are at least 10% higherthan the last ER change; (to allow for high inflationcountries)

Exclusion Window None Yes; 3 years

Indicators KLR (1998) + 2 more indicators (M2) (level) 3 macroeconomic, 4 external balance, 2 foreign andReserves and CA/GDP) binary base and weighted 7 composition of capital flowssum a la Kaminsky (1998)

Approach KLR (1998) signalling and probit model Probit

Results Signalling approach produces similar results as Output growth, DC growth, foreign interest rate, FDI/KLR (1998). The 2 new indicators noted above Total debt significant; reserves, RER somewhatare informative. Probit model shows that the significant?probability of crises increases when the followingvariable exceeds the thresholds: real exchangerate deviation, the CA, reserve growth, exportgrowth, level and growth rates of M2/reserves

Asian Crisis Results Both approaches perform significantly better Not good, according to BPthan pure guessing in terms of out-of-sampleprediction of the crisis in 1997

Notes Probit models seem to have better out-of- Many variables were unavailable for 1996, even assample predictability than KLR of mid-1998

Study Eichengreen, Rose, & Wyplosz (1996) Caramazza, Ricci, & Salgado (2000)

Dataset Quarterly data for 20 industrial countries f

05 res bhanumurthy

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