Clustering in Emerging Equity Markets

Abstract

We consider pairwise tail behavior of return series for identifying most important emerging markets clusters. Pairs of markets belonging to the same group present similar type and strength of interdependence during stressful times, represented by a common copula and a statistically equivalent measure of tail dependence. By collapsing data from d markets in a group we overcome the difficult problem of finding their (higher dimensional) d-variate distribution. Results may help portfolio managers to deal out risk due to comovements within clusters. We provide examples on how this can be done. Our study contribute on the discussion about international association among stock markets during turbulent periods, and do not confirm the intuition that the observed association between extremes should be credited to markets drivers.