Showing papers by "Blake LeBaron published in 2005"
TL;DR: In this article, the authors apply extreme value theory (EVT) to model the behavior of extreme events and compare tail thickness between emerging and developed market equity return distributions, and find mixed evidence for uniformity inside each region, and strong evidence for differences in tail behavior between developing and emerging regions.
Abstract: Extreme Value Theory (EVT) offers a powerful framework to characterize financial market crashes and booms. This paper applies EVT to model the behavior of extreme events and compares tail thickness between emerging and developed market equity return distributions. We extend previous results by augmenting parametric Monte Carlo tests with nonparametric bootstrap tests. We construct Monte Carlo and Bootstrapping experiments to estimate the statistical significance of differences in tail behavior between markets and regions. Within each market we find little evidence for asymmetry between positive and negative tails. We find mixed evidence for uniformity inside each region, and strong evidence for differences in tail behavior between emerging and developed regions. Our regional results have important implications for the expected diversification benefits of international portfolio allocation decisions.
56 citations
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TL;DR: In this paper, the authors apply Extreme Value Theory (EVT) to construct statistical tests of whether booms are more or less likely than crashes and whether emerging markets crash more frequently than developed equity markets.
Abstract: Equity market crashes or booms are extreme realizations of the underlying return distribution. This paper questions whether booms are more or less likely than crashes and whether emerging markets crash more frequently than developed equity markets. We apply Extreme Value Theory (EVT) to construct statistical tests of both of these questions. EVT elegantly frames the problem of extreme events in the context of the limiting distributions of sample maxima and minima. This paper applies generalized extreme value theory to understand the probability of extreme events and estimate the level of i?½fatnessi?½ in the tails of emerging and developed markets. We disentangle the major i?½tail indexi?½ estimators in the literature and evaluate their small sample properties and sensitivities to the number of extreme observations. We choose to use the Hill index to measure the shape of the distribution in the tail. We then apply nonparametric techniques to assess the significance of differences in tail thickness between the positive and negative tails of a given market and in the tail behavior of the developed and emerging region. We construct Monte Carlo and Wild Bootstrap tests of the null of tail symmetry and find that negative tails are statistically significantly fatter than positive tails for a subset of markets in both regions. We frame group bootstrap tests of universal tail behavior for each region and show that the tail index is statistically similar across countries within the same region. This allows us to pool returns and estimate region wide tail behavior. We form bootstrapping tests of pooled returns and document evidence that emerging markets have fatter negative tails than the developed region. Our findings are consistent with prevalent notions of crashes being more in the emerging region than among developed markets. However our results of asymmetry in several markets in both regions, suggest that the risk of market crashes varies significantly within the region. This has important implications for any international portfolio allocation decisions made with a regional view
14 citations