Following the collapse of the Thai baht’s peg on July 2, 1997, the financial markets of East and Southeast Asia—in particular, Thailand, Malaysia, Indonesia, the Philippines, and Korea—headed in a similar, downward direction during late 1997 and early 1998. The regional markets faced increasing pressure in the aftermath of the devaluation of the baht, and this pressure was reflected in the subsequent unraveling of the managed currencies in Malaysia and Indonesia. As the crises became full-blown, intense foreign exchange and stock market turmoil spread in the entire region, culminating in the collapse of the Korean won. News of economic and political distress, particularly bank and corporate fragility, became commonplace in the affected countries, and it appeared as though anything that brought one market down put additional pressure on the other markets as well.
What was the driving force behind this transmission of shocks from one country to the other? Was it fundamentals driven, or was it a case of irrational, herd mentality displayed by panic-stricken investors? Could the reaction of the markets simply be explained away by their historically close relationships? Finally, did some countries play a larger role in terms of cross-border impact than others? These questions provide the motivation behind this paper. We carry out three sets of analysis to tackle these issues. First, we use correlations and vector auto-regressions (VARs) to see the extent of comovement in the markets during the crises. Second, we test whether the correlations in these markets increased significantly during the crises. Finally, we estimate the impact of own-country and cross-border news on selected financial markets of the region.
We use three and a half years of daily data (1995–98) from the five selected countries for our empirical analysis. We first study the correlation between the countries of their respective foreign exchange, equity, interest rate, and sovereign debt markets, examining which markets seemed more affected and postulating why this was the case. We apply a VAR methodology to estimate the impulse responses to shocks in each of the currency and stock markets. This allows us to see if there was indeed significant transmission of pressure in the respective markets, as well as how persistent those shocks were.
Then, we test if the correlations in the various markets increase significantly during the crisis period in comparison to historical, “tranquil” period levels. If there is no significant increase in the correlation, then it is likely that the pressure felt by the markets is more due to some common cause or spillover effects. The policy implication would be to focus on the source of the shock and try to tackle that first. On the other hand, if the increase in correlation is significantly and substantially higher than the historical correlations, then there is reason to suspect that market fundamentals and/or sentiments have shifted, resulting in a different set of market dynamics. In such circumstances, there is an avenue for measures to calm the markets.
Finally, we distinguish between the impact of fundamentals and possible herd behavior on stock markets and exchange rates. But our use of high-frequency daily data limits our capacity to obtain many representations of fundamentals. We remedy this by creating a set of dummy variables to take into account the significant, market-moving news for the respective countries.
The dummy variables serve a dual purpose; they are proxies of own-country fundamentals, as well as a source of contagion for other countries. We estimate the impact of these dummies, as well as other selected fundamentals, on the financial markets through country-by-country regressions. We further our study by analyzing the residuals of these regressions to see the extent of cross-border correlation after controlling for fundamentals.
In addition, we compare the correlation result of our sample countries (the “Asia-5”) against a “control” group. For this purpose, we choose five European countries that were not undergoing any sort of financial crises, and check whether the market variables of the Asian crisis countries behaved differently than their European counterparts. We make our comparisons among the crisis group and the control group, and test for significant changes in correlations within the control group.
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Taimur Baig is an Economist in the European II Department. Ilan Goldfajn was an Economist in the Asia and Pacific Department when this paper was written; he is now Professor of Economics at the Catholic University of Rio de Janiero. The authors thank Andrew Berg, Tito Cordelia, David Goldsbrough, Laura Kodres, Paul Masson, Jonathan Ostry, Roberto Rigobon, and seminar participants at the IMF (Asia and Pacific Department), the World Bank (Research and Analytics Division), and the University of Illinois (Department of Economics) for helpful comments and suggestions. They also thank Ned Rumpeltin for data assistance.
With the exception of relatively small correlation between Indonesia and Korea (0.09).
For example, in Indonesia overnight interest rates were raised to 91 percent on August 20, 1997.
For brevity, we omit the rolling correlations for the interest rates; the results are not very instructive.
Although in crisis periods a significant liquidity premium can get incorporated into the spreads as well.
When compared with the correlations in June 1997, the Korea-Indonesia, Thailand-Indonesia, and Korea-Thailand correlations increase from 0.56, 0.60, and 0.22 to 0.95, 0.92, and 0.89, respectively, in the July–September window.
In the case of Korea, we chose the day the won began its downward fall.
The sample start dates for Thailand, Malaysia, the Philippines, Indonesia, and Korea were July 2, July 14, July 11, August 14, and November 6, respectively. All sample end dates are May 18, 1998.
To get a comparative idea about the behavior of the variables during the crisis and tranquil phase, see Baig and Goldfajn (1998).
The correlation results for the equity markets are available from the authors upon request.
As a logical extension of our work, we analyze the residual correlations of these cross-country dummy regressions. It is difficult to ascertain the informational content of these residuals. Having controlled for fundamentals and news from other countries, we have accounted for all tangible sources of market factors with daily frequency. The residuals of these regressions may contain unobserved movements of fundamentals, or pure contagion effects, or both. Given the unclear implication of the results, we omit them from this paper. It is worth noting that the exchange rate residual correlations diminish significantly relative to the correlations of own-country dummy regression residuals, whereas the stock market residuals remain fairly strong.