A COPULA-BASED CORRELATION MEASURE AND ITS APPLICATION IN CHINESE STOCK MARKET

Abstract

In this paper, a copula-based correlation measure is proposed to test the interdependence among stochastic variables in terms of copula function. Based on a geometric analysis of copula function, a new derivation method is introduced to derive the Gini correlation coefficient. Meantime theoretical analysis finds that the Gini correlation coefficient tends to overestimate the tail interdependence in the case of stochastic variables clustering at the tails. For this overestimation issue, a fully new correlation coefficient called Co is developed and extended to measure the tail interdependence. Empirical study shows that the new correlation coefficient Co can effectively solve the overestimation issue, which implies that the proposed new correlation coefficient is more suitable to describe the interdependence among stochastic variables than the Gini correlation coefficient.