Dependence Structures in Chinese and U.S. Financial Markets: A Time-Varying Conditional Copula Approach

Abstract

In this paper, we use a Time-Varying Conditional Copula approach (TVCC) to model Chinese and U.S. stock markets, dependence structures with other financial markets. The AR-GARCH-t model is used to examine the marginals, while Normal and Generalized Joe-Clayton copula models are employed to analyze the joint distributions. In this pairwise analysis, both constant and time-varying conditional dependence parameters are estimated by a two-step maximum likelihood method. A comparative analysis of dependence structures in Chinese versus U.S. stock markets is also provided. There are three main findings: First, the time-varying-dependence model does not always perform better than constant-dependence model. This result has not previously been reported in the literature. Second, although previous research extensively reports that the lower tail dependence between stock markets tends to be higher than the upper tail dependence, we find a counterexample where the upper tail dependence is much higher than the lower tail dependence in some short periods. Last, Chinese financial market is relatively separate from other international financial markets in contrast to the U.S. market. The tail dependence with other financial markets is much lower in China than in the U.S.