Since 1997, economists, policymakers, and journalists have talked about the “Asian flu.” It has generally been perceived that the adverse currency and stock market shock that first affected Thailand in July 1997 propagated across the world with little regard for economic fundamentals in the affected countries. Before the Asian flu, there was the 1994 Mexican “Tequila crisis,” and since then, the 1998 “Russian virus.” Emerging markets economic crises, in general, have been characterized as contagious. According to Webster’s dictionary, contagion is defined as “a disease that can be communicated rapidly through direct or indirect contact.” Emerging market economic crises have led to massive bailouts to quell contagion and have reduced support for free capital mobility. IMF deputy managing director Stanley Fischer rationalized the 1994 Mexican bailout in this way: “Of course, there was another justification: contagion effects. They were there and they were substantial.” Contagion has led Bhagwati (1998) and others to argue that “Capital flows are characterized, as the economic historian Charles Kindleberger of the Massachusetts Institute of Technology has famously noted, by panics and manias.” If markets work this way, it is not surprising that Stiglitz (1998) called for greater regulation of capital flows, arguing that “…developing countries are more vulnerable to vacillations in international flows than ever before.”
Even though this contagion connotes powerful images of economic and financial plagues, it is difficult to study scientifically. Evidence of this difficulty is that there is little agreement on even defining what financial contagion means. Since equity market valuations reflect future economic activity, much of recent research attempts to learn about contagion by investigating whether equity markets move more closely together in turbulent periods. There are considerable statistical difficulties involved in testing hypotheses of changes in correlations across quiet and turbulent periods and recent investigations of this issue find at best mixed results. Nevertheless, there does not seem to be strong evidence that stock returns in one country are more highly correlated with returns in other countries during crisis periods once one takes into account the fact that the conditional correlation of stock returns is higher during such periods even if the unconditional correlation is constant. A related literature demonstrates that, even though correlations change over time, it is difficult to explain changes in correlations.
Investigations of contagion focus on asset return correlations, a linear measure of association, likely because most international asset pricing models start with a linear factor structure as a first principle. The factors are presumed to capture all of the important comovements in asset returns and are obtained empirically using factor analysis or principal components methods or simply by pre-specifying macroeconomic variables as instruments. The problem with such investigations is that none of the concerns expressed about contagion seem to be based on a linear measure of association for returns. These concerns are generally founded on the presumption that there is something different about extremely bad events that leads to irrational outcomes, excess volatility, and even panics. In the context of stock returns, this means that if panic grips investors as stock returns fall and leads them to ignore economic fundamentals, one would expect large negative returns to be contagious in a way that small negative returns are not. Correlations that give equal weight to small and large returns are not appropriate for an evaluation of the differential impact of large returns. It could be that large shocks, because they exceed some threshold or generate panic, propagate across countries, but this propagation is hidden in correlation measures by the large number of days when little of importance happens.
To address these concerns, a number of recent studies have extended models of international asset return correlations to allow for these observed threshold (large) and asymmetric (negative return) effects. Some researchers have employed multivariate extreme value theory from statistics (Longin and Solnik, 2001; Straetmans, 1998; Starica, 1999; Hartman, Straetmans and de Vries, 2001). Others have developed multivariate GARCH-M models allowing asymmetry (Bekaert and Wu, 2000; Ang and Chen, 2000), Poisson jumps (Das and Uppal, 1999), and even Hamiltonian regime-switching (Ang and Bekaert, 2000) in the joint dynamics of returns. By contrast, in this paper, we abandon the correlation framework that previous researchers have focused on to study contagion and direct our attention instead to the large absolute value daily returns. To avoid a situation where our results are dominated by a few observations, we do not compute correlations of large returns but instead measure the joint occurrences of large returns. We show that linear models cannot explain the patterns that we observe for large absolute value returns; that is, there are more frequent joint occurrences of large absolute value returns than linear models would predict. We then develop an econometric model of the joint occurrences of large absolute value returns using multinomial logistic regression.
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