Managerial FinanceAsian financial crisis: the pre‐ and post‐crisis analysis of Asian equity marketsAmitava ChatterjeeO. Felix AyadiBalasundram Maniam

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Asian Financial Crisis: The Pre- and Post-CrisisAnalysis of Asian Equity Markets

by Amitava Chatterjee, JHJ School of Business, Texas Southern University, Houston, TX77004, USA, O. Felix Ayadi, JHJ School of Business, Texas Southern University, Hous-ton, TX 77004, USA, and Balasundram Maniam, Department of General Business andFinance, Sam Houston State University, Huntsville, TX 77341, USA.

Abstract

This study adds to the ongoing analysis of the long-term impact of Asian financial crisison the stock markets of eight Asian-Pacific countries. Using current data to capture post-crisis behavior of returns, multivariate cointegration analysis reveals that a cointegratingrelationship exists among the markets that transcend the financial crisis. Both vector errorcorrection (VEC) and Granger causality tests demonstrate the profound effect of financialcrisis in Korea on the returns of other countries. Granger causality tests further reveal thatthe events surrounding the crisis in Thailand and Indonesia largely dictate their ownshort-run returns behavior since the advent of the crisis. Compared to earlier period, thepost-crisis era also experiences a closer relationship among the index returns of HongKong, Korea, and Singapore and a heightened degree of convergence among the returnsof Asian markets.

I. Introduction

Beginning in 1997, the East Asian national currencies collapsed violently and the tigereconomies fell into a crippling recession after a miraculous decade of growth and expan-sion. Precise determination of the beginning and ending time frames of the overall Asianfinancial crisis as well as to reflect upon the causes of the crisis is as diverse as thenumber of studies that follow the crisis. In general, however, most of the recent literatureseem to agree on three broad reasons for the recent Asian financial crisis.

Recent articles by Corsetti, Pecenti, and Roubini (1999) and Alon and Kellernan(1999) unveil a story of contagion effect in the Asian economies, a concept reminiscentof the 1960’s Domino Theory. The effect allows economies that are geographically andstructurally adjacent to follow each other throughout the business cycle. Countries lo-cated within a given region, particularly emerging nations, normally rise and fall togethereconomically. Since the Asian tiger economies have maintained high rates of growth dur-ing the 1990’s, investor concern is evident that if one regional economy collapses, the restwould follow suit rapidly.

In January 1997, the Hanbo Steel Corporation of South Korea defaults on a loan,thus beginning a string of major business failures in that country. In May, speculators be-gin to feel uneasy about economic and political fundamentals in Thailand and initiatecapital outflows bringing the baht under pressure. At the end of June, the political estab-lishment in Thailand tries to provide assurance to both the nation and to the world thatthere would be no devaluation of the Thai currency; but days later the first domino falls.

During the first week of July, The Bank of Thailand announces the abandonment ofthe currency peg (primarily to the US dollar) in favor of a managed float of the Baht - ef-

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fectively devaluing that currency by approximately 20%. Within a fortnight, investorconfidence in the Asian region makes a rapid deterioration. The Philippine peso, the Ma-laysian ringgit, and the Singapore dollar are allowed to float and immediately their valuestake a slump. In mid-August, the Indonesian rupiah also floats and its value takes a down-ward plunge. In October, the Hong Kong stock market loose nearly 25% of its value dur-ing a three-day period. In mid-November, the South Korean won is allowed to float,followed by a sharp drop in its value.

Alarmed by these chain of events, the International Monetary Fund (IMF), workingin coordination with the World Bank and the Asian Development Bank, executes multi-billion dollar bail-out plans of cash and credit for Thailand, The Philippines, Indonesia,and South Korea. Concurrent with the floating of the Baht in July 1987, Thailand be-comes the first country to request new technical assistance from and to begin negotiationswith the IMF. In mid-July 1997, the IMF augments an existing agreement with a US $1.1billion loan package to The Philippines. In August 1997, the IMF approves a US $17 bil-lion package for Thailand. At the end of October 1997, the IMF and Indonesia has agreedto an initial financing package of US $43 billion. At the beginning of December 1997, theIMF and Korea settled to an initial financing package of US $58 billion. By the end ofAugust 1998, over US $28 billion has been disbursed: The Philippines US $1.1 billion;Thailand US $1.2 billion; Indonesia US $6.0 billion; South Korea US $18.1 billion.

Various authors discuss a score of reasons for the Asian financial crisis, but allseem to agree on three primary issues:

(1) Excessive short-term borrowing of foreign funds pairs with excessive long-term lending in the domestic economy that resulted in failure to match loanmaturities,

(2) Weak financial structure as displayed by poor risk management by banks andlax supervision by authorities, and

(3) Moral hazard and government intervention in lending.

The first reason floated by the authors can be thought of as a case of simple eco-nomics: the laws of supply and demand. A huge supply of foreign funds has been avail-able to the Asian region financial intermediaries most particularly because of thesustained high economic growth rates within the region, the lower inflation and lower in-terest rates in other parts of the world, and the higher interest rates within domestic mar-kets because of the high demand for capital for investment projects. According to Yeh,Suwanakul, and Chun (1999), international lenders do not normally lend money forlong-term investment projects; thus the bulk of the foreign monies borrowed by the Asianregion banks are on short-term contracts, normally one year. The regional banks makeloans to domestic firms, however, for long-term projects such as manufacturing capacityand national infrastructure. The regional banks fail to match loan maturities for borrow-ing and lending properly, a failure that is shared by the international lenders because oftheir lack of monitoring.

The second reason involves credit quality and regulatory oversight. The Asian re-gional banks exhibit poor risk management by their disregard for the credit-worthiness ofthe borrowers. In the context of developing economies, rapid credit growth is often asso-ciated with credit quality problems. Yeh, Suwanakul, and Chun (1999) note that loans are

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made on the basis of customer relationship and collateral, not credit analysis. Issuing andincreasing credit based on the value of collateral is closely associated with asset pricebubbles (equities, commercial property, real estate) that exacerbate the non-performingloans of the regional banks. When the bubbles burst, the fairly illiquid assets that collater-alize the loans are not worth the loan values (Sarno and Taylor, 1999). While East Asiancountries have fairly elaborate bank regulation and supervisory regimes, almost all thebanks are either government owned and/or fall under heavy government influence. Thesetup provided enough incentives for bank managements to overextend domestic credit(Barth et. al., 1998).

Third, the literature defines moral hazard as the perception of guarantees for finan-cial institutions by governments and/or guarantees for national economies by interna-tional agencies such as the IMF and the World Bank. Increasing the moral hazardproblem during the period of crisis is the practice of regional governments intervening inor actually directing the national banking sectors that give investors and depositors im-plicit and sometimes-explicit repayment guarantees. Political connections and cronyismare particularly important since most of the Asian commercial banks are state-owned.Chen (1999) shows that moral hazard permits the huge inflows of international capital tothe Asian tiger economies without the explicit and implicit guarantee of various govern-ment agencies on the credit risk of foreign loans, causing the interest rate on foreign loansas the same as domestic loans thereby eliminating the incentive to borrow foreign loanson a great scale.

IMF financial assistance has become predictable over the years by allowing partiesof lenders and investors to form expectations of bailouts. Bashir and Ghebreyesus (1998)discuss the problem of trans-national agency moral hazard where a lender-of-last-resortinstitution like the IMF is needed to inoculate a region against contagion by providing im-mediate cash liquidity without weighing against the moral hazard it creates by encourag-ing unwise risk-taking.

This study seeks to investigate the equity markets of Asian economies in the lightof the financial crisis of 1997. Analyzing the long-term trends in their stock markets be-fore, during, and after the crisis captures the effect of the crisis. The rest of the paper is or-ganized as follows. Section 2 describes the data and section 3 discusses the methodology.Section 4 analyzes the findings, while section 5 provides some conclusions and recom-mendations.

II. Data Description

The data set consists of close-to-close daily stock indices for eight Pacific-Basin markets,such as: Hong Kong, Indonesia, Malaysia, Philippines, Singapore, South Korea, Taiwan,and Thailand. For each country, the index series run from April 1990 to March 2001, pro-viding data points for eleven years. In total, the data set consists of 2756 observations foreach country. The effect of Asian financial crisis is investigated by dividing the data setinto two sub-periods. The pre-crisis returns behavior is analyzed on the first sub-sampleranging from 4/3/90 to 12/31/96, providing 1761 observations. The period of the finan-cial crisis and its aftermath is captured through the second sample running from 6/2/97 to3/26/01 with 995 observations.1 The data is collected from the Datastream. The time pe-riod under study is chosen in such a way as to encompass the Asian financial crisis fromboth ends and allow this paper to investigate its impact.

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The stock indices are converted into continuously compounded rates of return bytaking the difference between natural logarithms of two consecutive daily index prices.The series is expressed as:

R I Iit it i t� � �ln( ) ln( )( )1 (1)

Where, Iit is the respective stock price index of the i-th country at time t. Rit is thecorresponding rate of return on the stock index. The logarithmic forms are used as mosteconomic and financial time series follow curvilinear trends. Summary statistics of indexreturn series are provided in Table 1.

The observed skewness statistics are non-zero for the return series for all countries,indicating skewed returns distribution for all markets. The kurtosis statistics for all indexreturns are large, positive, and significant at the one percent level. The excess kurtosisstatistics indicate that the distributions are leptokurtic relative to the normal distribution.The Jarque-Bera test statistics rejects the null hypothesis of normality at the one percentlevel of significance for all series. The rejection of normality for the unconditional distri-butions of returns (common to almost all returns of speculative assets) makes one to sus-pect the intertemporal dependencies in return series (i.e., the data series are notindependent and identically distributed).

III. Methodology

A precondition for the cointegration of time series is for the series to be integrated of thesame order. In other words, if two series are cointegrated of order d (i.e. I(d)) then eachseries has to be differenced times to restore stationarity. For d = 0, each series would bestationary in levels, while for d = 1, first differencing is needed to obtain stationarity. It isimportant to convert non-stationary variables into stationary processes. Otherwise, theydo not drift toward a long-term equilibrium.

Two most commonly used approaches to test for stationarity are AugmentedDickey and Fuller (ADF) test, proposed by Dickey and Fuller (1979), and the Phillips-Perron (P-P) test, proposed by Phillips and Perron (1988). These tests are referred to asunit-root tests as they test for the presence of unit roots in the series. These tests correctfor any serial correlation that might exist in the series by including lagged changes of theresidual in the regression. The test procedure is represented by the following expressions:

� �z t z zt t i t i t

i

q

� � � �� ��

� � �11

(2)

Where, � represents the difference operator with � = (1-L) and L is the lag opera-tor. Therefore, �zt = (1-L)zt = zt-zt-1 and q represents the number of lags necessary to en-sure that �t is empirically white noise. Here, zt is the series under investigation and t is thetime trend. Engle and Yoo (1987) suggest the use of the Modified Akiake InformationCriterion (MAIC) in order to select an optimum number of lags. The MAIC is defined as:

MAIC k NSSR

Nk( ) ln� �

�

�

!

"#�2 (3)

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Here, MAIC (k) is the modified Akaike Information Criterion, N is the number ofobservations, represents the sum of squared residuals from an OLS regression on equa-tion 2, and k(= q + 1) is the number of parameters in equation 3. The procedure computesMAIC over different values of q and selects a q that minimize the value of MAIC.

Under the ADF test, the null hypothesis of a unit root is rejected if the calculatedpseudo-t values (ADF statistic) lie to the left of the relevant critical value. The underlyingcritical values are provided in MacKinnon (1991). The specification of P-P test is thesame as ADF test except that P-P test also adjusts for error autocorrelation.

The cointegration method assumes that if any two variables contain a common sto-chastic trend, each can be described as an integrated variable. Also if a linear combinationof these two variables is observed stationary, the two variables are cointegrated. Underthe bivariate framework, the cointegrating regression equations between any two marketindex returns with reverse specifications can be described as follows:

R R et t t1 0 1 2� � �� � (4)

R R vt t t2 0 1 1� � � (5)

Where R1t is the monthly index return of market 1, R2t is the return from stock mar-ket 2, and et , vt are the stochastic error terms. The cointegrating equations are estimatedby ordinary least squares (OLS) to investigate whether these two returns are cointegrated.The residuals � ( � � ) � ( � � )e R R v R Rt t t t t t

� � � � � �1 0 1 2 2 0 1 1� � and are then used to test

whether R Rt t1 2and are cointegrated. This paper uses the cointegration test proposed by

Johansen and Juselius (1990) and Johansen (1991). The advantage of this procedure liesin its use of maximum likelihood estimation both within the bivariate and multivariateframeworks. The model calculates and tests the number of cointegrating vectors in anOLS setup. If the null hypothesis that the residuals are integrated cannot be rejected, thetwo series are cointegrated. If R R

t t1 2and are cointegrated (the number of cointegratingequations are less than two), the correlation between two returns increases in the long run.

Using a similar argument, the multivariate cointegration equation with reversespecifications for any number of series can be represented by:

R R ut i it t

i

n

1 02

� � ��

�� (6)

Where, R1t represents the returns on the i - th index and i is the corresponding re-gression coefficient.2 For diversification potential in n different markets ( .... )R i n

i$ �1 to

exist, the number of common trends (the number of cointegrating equations) has to beequal to the number of return series (i.e., n index returns’ series will be dominated by ndifferent stochastic trends). In that case, all the returns’ behavior could be considered asrandom walks. Consequently, the correlations between these market return series will notbe dependant upon the length of the investment horizon.

If the return series are cointegrated, Granger representation theorem states that anerror correction model (ECM) describes the dynamic relationship. The advantage ofECM framework lies in its strength of capturing both the short run dynamics and long runequilibrium relation between the two series. Durr (1993) observes that the error correc-

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tion models are appropriate when the dependent variable is known to exhibit short runchanges in response to changes in the independent variable. The error correction modelfor the cointegrating index return series of county 1(R1t) is expressed as follows:

� �R W R Rt i i t p t p jq j t q t

q

n

j

s

1 1 1 1 112

� � � �� � ���

�� % & �( ) ( ) ( )����� p

m

i

r

11

(7)

Here, Wi(t-1) represents the error correction term from the i - th cointegrating equa-tion and � � represents the corresponding parameter. It represents the extent to which anytemporary dis-equilibrium in the short-run is corrected. Also, %1 p

represents the parame-ter of own-country p - th lagged return under estimation; while &

jqis the parameter esti-

mate of the q - th lagged return of the j - th country and �1t is the white noise error of theindex return series of county 1. If a total of s number of series are cointegrated, the ECMspecification shows that the changes in Rjt (the return from j - th series) depend on thechanges in itself as well as on the lagged values of all other cointegrating return series. Itpartially corrects the dis-equilibrium from the cointegrating relationship between the co-integrating variables.

The short run dynamics among cointegrating returns are observed by &jq

s. If any ofthe &

jqcoefficients is nonzero and statistically significant, the j - th series movement is

said to have a short run impact on index return movements of all other countries. Further-more, if jointly (| | | | .... | | )� � �1 2 0� � � �r and it is statistically significant, the correspond-ing series will move toward a long-term equilibrium relationship (cointegrated). Asproposed by Engle and Granger (1987), The error correction model (ECM) is estimatedwithin the vector autoregression (VAR) framework.

Granger causality test allows the framework to test for the presence of uni- and bi-directional causality. It essentially examines the predictive abilities of time series models.The index return series of i - th country [Rjt] causes the return series of j - th country [Rjt]in the Granger sense, if the current value of Ri can be predicted better by using the pastvalues of Rj than by not doing so, considering all other relevant information including thepast values of Ri. The uni- and bi-directional relationships are expressed as follows:

R R Rjt k i t k

k

m

q j t q t

q

m

� � � ���

��

� �� � �01 1

( ) ( ) (8)

R R Rit l j t k

l

m

q i t q t

q

m

� � � ���

��

� �� � �01 1

( ) ( ) (9)

In general, Rj is said to Granger cause Ri, provided that some of the ak s are non-zero. If, simultaneously, Ri Granger causes Ri for some non-zero �i s, there appears tohave a feedback relationship. The test of causality is based on an F-statistic by estimatingthe expressions in both restricted and unrestricted forms.3 The test statistic is expressed asfollows:

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F

SSE SSEm

SSET m

R UR

UR

�

�

� �

( )

( )2 1

(10)

Here, SSER and SSEUR are residual sum of squares of the restricted and unrestrictedmodels respectively, T is the total number of observations, and m represents the number

of lags. The F-statistic follows a� 2

m distribution.

IV. Empirical Findings

In line with Granger (1986), a necessary but not sufficient condition for cointegration isthat each of the series has to be integrated of the same order. Accordingly, each index re-turns series is tested for stationarity. The null hypothesis for the presence of a unit root(the series is non-stationary) is tested using ADF and P-P tests both with and without atime trend. The stationarity test results are presented in Table 2.

The tests are applied to both levels of the series as well as to first differences. Usingthe Modified Akaike Information Criterion (MAIC) chooses the optimal lag length foreach series. For series levels, the null hypothesis of non-stationarity (the existence of aunit root) cannot be rejected under the two tests. However, for the first differences, thenull hypothesis of two unit roots is rejected in all index returns by both tests. Therefore, itis concluded that the index returns are non-stationary in levels but the first differences arestationary. Subsequently, the model estimations of anomalous behavior in the equity in-dices are based on stationary returns (after all the series are expressed in their first differ-ences).

Results from the Johansen multivariate cointegration tests are reported in Table 3.The analysis is conducted for the total period under investigation as well as for the sub-periods of pre-crisis and of the during- and post-crisis eras. In all cases, a single cointe-grating equation is observed to determine the long-term co-movements of the return se-ries of respective periods. The finding implies that a single long-term relationship existsamong the stock markets under consideration that transcends the financial crisis. Thefinding of the existence of cointegration among the returns during the pre-crisis periodcontradicts the observations by Sheng and Tu (2000) but conforms to that of Tan (1999)and Masih and Masih (2000).4 The results further demonstrate that market oriented re-forms and restructuring in the 1990s bring the equity markets of the Pacific-Basin coun-tries closer together, and the co-movements of the stock prices persist even beyond thecrisis.

Since the index returns series of the Asian markets exhibit cointegrating relation-ships, a vector error correction (VEC) framework can be estimated to model the short-term dynamics that characterize the system. Further, the size of the cross-country error-term coefficients ( )&

jqs measure the speed at which a given return series adjust to the

change in equilibrium (cointegrating) conditions. Results from the Vector Error Correc-tion tests are reported in Table 4. In order to quantify the extent of short-term effect onemarket poses on another, the test is performed on the total period under study and on twosub-periods of pre- and post-crisis return series.

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For the total period analysis, the test reveals that returns in the Hong Kong marketare significantly affected by the lagged returns in the Korean and Thailand markets, whileonly the lagged returns in the Malaysian market have any effect on the Taiwanese equityreturns. There exists a significant feedback relationship between the returns of Philip-pines and Indonesia, and their returns are also significantly affected by the lagged returnsof other ASEAN (Association of South East Asian Nations) members, such as Malaysia,Singapore, and Thailand (but not vice versa). Malaysian returns are affected by thelagged returns of Korea, while the short-term impact on the index returns of Thailandemanated from the lagged returns of Korea and Singapore. The observation further re-veals that almost all the significant error correction coefficients are positive, implying adirect relationship among the returns of Asian equity markets. Barring six coefficients,however, all other error correction coefficients are found to be small in magnitude (lessthan 0.10).5 The findings demonstrate a slow adjustment process (error correction) of thereturns toward a change in the equilibrium conditions.

In the light of cointegrating relationship, the impact of Asian financial crisis on theequity returns is captured through separate VEC analyses of pre- and post-crisis returns.The pre-crisis investigation of returns reveals that a significant feedback relationship ex-ists among Thailand, Philippines, and Singapore, while a similar relationship is absentduring- and post-financial crisis period. In the pre-crisis period, Taiwanese returns havethe least level of association with the returns of other Pacific-Basin countries and thattrend continues through the crisis period and beyond. The finding validates the fact thatTaiwan has been one of the least affected economies by the financial crisis. The crisis andpost-crisis analysis further reveals that Korean returns play an important part in produc-ing significant short-run dis-equilibrium for the returns of six out of seven other countriesunder investigation. It demonstrates the paramount effect the outbreak of financial crisisin Korea has on other countries in the region that continues beyond the crisis period. Sur-prisingly, the returns from Thailand (the place of origin of the Asian financial crisis) failto divulge similar trend. Similar to total period analysis, all the significant short-runspeed-of-adjustment (error correction) coefficients from both sub-period analyses arepositive, implying a direct relationship among the index returns. Barring five coefficientsin both the pre-crisis and during- and post-crisis analyses6, all other error correction coef-ficients are small in magnitude. Overall, the VECM fails to reveal any fundamental shiftin short-run returns behavior due to the financial crisis except to divulge an increasingimpact of Korea (one of the most heavily affected economy by the crisis) on otherPacific-Basin economies in the aftermath of crisis.

Granger (1988) points out that a cointegrating relationship among variables mustexhibit causal relationship among those variables at least in one direction. In that light,the Granger causality test is performed on the total sample as well as on the sub-samplesof pre- and post-crisis periods analyses. The results are reported in Table 5. The compari-son of results from the total period analysis with those of pre-crisis period and during-and post-crisis period reveal a few interesting facts. The findings are summarized in sub-sequent paragraphs.

Significant bi-directional causality (feedback) is observed to exist among the pairsof Korea – Hong Kong, Singapore – Hong Kong, Singapore – Korea, Thailand – Korea,and Singapore – Malaysia for both the total- and post-crisis period analyses, but not dur-ing the pre-crisis period. It indicates a significantly closer relationship among the returnsof these markets since the advent of the financial crisis. Tuluca and Zwick (2001) observe

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a similar trend of sudden increase in feedback relations and an increase in co-movementduring the post-crisis interval. Comparable movements are also observed in uni-directional causal relationships where return series in Korea precedes those of Indonesia,Malaysia, Philippines, Taiwan, and Thailand. It conforms to earlier VECM analysis thatcrisis in Korean economy has a profound effect on the returns of other countries. Similarto Alexakis and Siriopoulos (1999) and In, Kim, Yoon, and Viney (2001), the findings in-dicate a convergence of Hong Kong returns to those of other Asian markets surroundingthe crisis.

On the other hand, two-way causal relationship exists among the pairs of HongKong – Malaysia, Indonesia – Singapore, Thailand – Malaysia, Philippines – Thailand,and Thailand – Singapore during the pre-crisis period, but not during the crisis and be-yond.7 Similarly, the uni-directional evidence of Malaysia, Singapore, and Taiwan lead-ing Indonesia during the pre-crisis period are largely absent since the advent of the crisis.Overall, the findings indicate that the events in Indonesia and Thailand (two of the econo-mies that are severely affected by the crisis) during the crisis mostly dictate their own re-turns behavior than to be influenced by others.

V. Conclusion

The main purpose of this study is to examine the impact of Asian financial crisis on theindex returns series of eight Pacific-Basin economies. The paper employs Johansen mul-tivariate cointegration technique, Vector Error Correction Model (VECM) framework,and Granger Causality tests to investigate the impact of the crisis. To that effect, separateanalysis is performed for the total period as well as for the sub-period of pre-crisis and forthe during- and post-crisis sub-period to capture the impact of crisis on the index returns.As a precondition for the test of cointegration, each return series is also tested for station-arity and the results indicate that all the market return series indicate stationarity after firstdifferencing.

The cointegration analysis indicates a single cointegrating relationship among thereturn series for each of the sub-periods as well as for the total period under investigation.The findings strongly point toward a long-term relationship among the markets that tran-scends the financial crisis. The Vector Error Correction Model (VECM) is employed tocapture both the short-run dynamics and long-run equilibrium relation among the series.The results reveal that a feedback relationship that has existed prior to the crisis is largelyabsent since its advent. The analysis further shows that the crisis in Korea (but not inThailand) has a significant impact on the returns of other countries that continues beyondthe crisis period.

The Granger causality test results indicate a significant closer (two-way causality)relationship among the returns of Hong Kong, Korea, and Singapore since the crisis. Thefindings compliment earlier observations that emphasize the profound effect of Koreancrisis on the returns of other countries. On the other hand, the close relationship amongthe returns of Indonesia, Philippines, and Thailand, and the trend of Malaysia, Singapore,and Taiwan leading Indonesian returns during the pre-crisis period are largely absent dur-ing the crisis and its aftermath. On a short-term basis, it indicates that the event in Indone-sia and Thailand during the crisis mostly dictate their own returns behavior beforereverting towards the long-term linkages among the Asian equity markets. Overall, thefindings strongly indicate that market oriented reforms and restructuring and increasing

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globalization of the financial markets in the 1990s bring the equity markets of thePacific-Basin countries closer together. In essence, the co-movement of Asian stock mar-kets remains as strong as ever even through the Asian financial crisis. Studies by Al-exakis and Siriopoulos (1999), Tan (1999), Sheng and Tu (2000), In, Kim, Yoon, andViney (2001), and Tuluca and Zwick (2001) observe a similar trend of increased conver-gence of returns among the Asian markets since the advent of the crisis.

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Endnotes

1. Many authors attribute the direct beginning of the crisis to the collapse of the Thai bahton July 2, 1997. However, their observations seem to agree that the crisis has been her-alded earlier, especially in the month of June when the stock price in Thailand experiencea sharp and dramatic fall. Accordingly, this study uses the returns data in June 1997 as thestarting point to investigate the crisis. The data points from January 1997 to May 1997 areomitted from the study to distinct the sub-periods from one another.

2. With reverse specifications, the complete system is represented by n equations. Here,Rit, �0 and ut are [ 1�n ] vectors, while i is a [ n n� ] matrix.

3. A detailed description of the Granger causality test is provided in Alexakis and Sirio-poulos (1999).

4. Sheng and Tu (2000) find cointegration among the returns in the aftermath of, but notbefore the crisis. Both Sheng and Tu (2000) and Tan (1999) observe a contagion effect (areduced degree of exogeneity of returns) during the financial crisis. Masih and Masih(2000) find similar trend even during the pre-crisis period.

5. Higher speed-of-adjustment coefficients are observed for the lagged returns of Koreaand Philippines on Indonesia and lagged returns of Singapore on Korea, Philippines, Tai-wan, and Thailand.

6. In the pre-crisis period analysis, the speed-of-adjustment coefficients are higher for thelagged returns of Malaysia on the returns of Indonesia and Thailand and the lagged re-turns of Singapore on Philippines and Thailand. The analysis of the crisis period and be-yond has error correction coefficients that are higher for the Korean lagged returns onIndonesia and Malaysia and the lagged returns of Singapore on Korea, Philippines, andThailand.

7. The analyses of these pairs indicate that only one-directional causal relationships existin the post-crisis period where Indonesia leads Singapore; Philippines precedes Taiwan,and Singapore leads Thailand.

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TABLE 1

SUMMARY STATISTICS OF DAILY INDEX RETURNS OF EIGHT ASIAN COUNTRIES

(TOTAL PERIOD ANALYSIS: 04/1990 – 03/2001)

StatisticHong

KongIndonesia Malaysia Philippines Singapore

South

KoreaTaiwan Thailand

Mean 0.000484 -0.000786 0.000032 -0.000017 0.000053 -0.000183 -0.000156 -0.000226

Median 0.000114 0.000113 0.000045 0.000008 0.000051 0.000076 -0.000029 -0.000315

Maximum 0.155611 0.522480 0.229868 0.195485 0.106183 0.268353 0.137329 0.163520

Minimum -0.135787 -0.529450 -0.367680 -0.097124 -0.095464 -0.216509 -0.122979 -0.158925

S.D. 0.016878 0.032852 0.020619 0.017851 0.013144 0.025896 0.021931 0.023070

Skewness -0.072763 -0.638914 -1.298079 0.680511 0.042183 0.398491 -0.024982 0.432251

Kurtosis 11.74227* 74.12948* 56.93687* 13.58260* 10.36681* 14.69621* 6.105032* 9.109193*

Jarque-Bera 9126.025* 604161.1* 348088.6* 13590.11* 6479.323* 16406.47* 1151.219* 4544.558*

Observations 2865 2865 2865 2865 2865 2865 2865 2865

IndexHangSeng

JakartaComposite

KualaLumpurComposite

PhilippinesComposite

StraitsTimesIndustrial

KoreaComposite

TaiwanWeighted

Thai SET

Note: The * indicates significance at the 1% level. The 1% critical value for the coefficients of Skewness andKurtosis is 2.576. Jarque-Bera is a test statistic for testing the normality of the return series. The statistic is

distributed as a 2χ distribution with 2 degrees of freedom.

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TABLE 2

UNIT-ROOT TESTS FOR STATIONARITY OF STOCK INDEX RETURNS

1 11

q

t t i t t

i

z t z zλ ρ β ε− −=

∆ = + + ∆ +∑ADF P-P

CountriesOptimum

Lag Length Without

TrendWith Trend

Without

TrendWith Trend

HONG 12-1.990662(-3.432452)

-1.671190(-3.961254)

-2.031260(-3.432442)

-1.791779(-3.961247)

�(HONG) 14-12.33436*(-3.432454)

-12.39065*(-3.961263)

-51.16801*(-3.432443)

-51.19969*(-3.961248)

INDO 12-0.492304(-3.432452)

-1.197375(-3.961260)

-0.568706(-3.432442)

-1.225012(-3.961247)

�(INDO) 20-10.00125*(-3.432459)

-10.02606*(-3.961270)

-56.57977*(-3.432443)

-56.56825*(-3.961248)

MALY 20-1.491974(-3.432458)

-1.515572(-3.961269)

-1.423882(-3.432442)

-1.436408(-3.961247)

�(MALY) 17-11.33311*(-3.432457)

-11.38062*(-3.961267)

-48.13471*(-3.432443)

-48.13232*(-3.961248)

PHIL 13-1.286254(-3.432453)

-0.882848(-3.961260)

-1.035966(-3.432442)

-0.591423(-3.961247)

�(PHIL) 17-10.00678*(-3.432457)

-10.08506*(-3.961267)

-44.85690*(-3.432443)

-44.88612*(-3.961248)

SING 1-1.773595(-3.432443)

-1.506920(-3.961248)

-1.718255(-3.432442)

-1.437357(-3.961247)

�(SING) 17-10.98004*(-3.432457)

-11.02290*(-3.961267)

-47.38009*(-3.432443)

-47.40727*(-3.961248)

KORE 26-2.078561(-3.432463)

-2.103405(-3.961276)

-1.802675(-3.432442)

-1.825113(-3.961247)

�(KORE) 24-9.135483*(-3.432462)

-9.134627*(-3.961275)

-49.94974*(-3.432443)

-49.94110*(-3.961248)

TAWN 18-2.168083(-3.432457)

-3.100065(-3.961249)

-2.086170(-3.432442)

-3.130907(-3.961247)

�(TAWN) 27-8.746096*(-3.432465)

-8.751150*(-3.961278)

-51.76640*(-3.432443)

-51.77107*(-3.961248)

THAI 15-0.818598(-3.432454)

-1.441968(-3.961263)

-0.612546(-3.432442)

-1.258582(-3.961247)

�(THAI) 21-9.876920*(-3.432460)

-9.972512*(-3.961271)

-46.51784*(-3.432443)

-46.52995*(-3.961248)

Note: ADF is the Augmented Dickey-Fuller test statistic and P-P is the Phillips-Perron teststatistic.HONG, INDO, MALY, PHIL, SING, KORE, TAWN, and THAI represent the teststatistics for the index return series and �(HONG), �(INDO), �(MALY), �(PHIL),�(SING), �(KORE), �(TAWN), and �(THAI) represent the test statistic for firstdifferences of the index return series of Hong Kong, Indonesia, Malaysia, Philippines,Singapore, South Korea, Taiwan, and Thailand respectively.1% MacKinnon critical values for the rejection of hypothesis of a unit-root areprovided in parentheses and * represents the significance at 1% level.

� �z t z zt t i t i t

i

q

� � � �- -

=

� � �1

1

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TABLE 3

MULTIVARIATE JOHANSEN COINTEGRATION TEST

1 02

n

t i it t

i

R R uγ λ=

= + +∑PANEL A

(TOTAL PERIOD ANALYSIS: 04/1990 – 03/2001)

Cointegrating Equation Eigen Value Maximum Eigen value

Statistic

None 0.02441370.68857**(60.81, 54.25)

At Most 1 0.01462942.14699(54.48, 48.45)

Max-Eigenvalue test indicates One cointegrating equation at both 5% and 1% levels.

PANEL B

(PRE-CRISIS ANALYSIS: 04/1990 – 12/1996)

Cointegrating Equation Eigen Value Maximum Eigen value

Statistic

None 0.04042472.45852**(60.81, 54.25)

At Most 1 0.02185638.80490(54.48, 48.45)

Max-Eigenvalue test indicates One cointegrating equation at both 5% and 1% levels.

PANEL C

(DURING- AND POST-CRISIS ANALYSIS: 06/1997 – 03/2001)

Cointegrating Equation Eigen Value Maximum Eigen value

Statistic

None 0.06223070.61190**(60.81, 54.25)

At Most 1 0.02928832.66773(54.48, 48.45)

Max-Eigenvalue test indicates One cointegrating equation at both 5% and 1% levels.

Note: Test assumption: Linear deterministic trend in the data. The test starts from the most

basic null hypothesis ( )0H of no Cointegration. If 0H is rejected, the test proceeds to

the next row that tests for the 0H of at most 1 cointegrating equation. It continues until

0H may no longer be rejected.

The ** and * denote rejection of the hypothesized maximum number of cointegratingequation(s) at 1% and 5% significance levels respectively.1% and 5% critical values for the rejection of hypothesis are in the parentheses.

R R ut i it t

i

n

1 0

2

� � �=

��

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