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In this article, we use a time-varying conditional copula approach to model Chinese and US stock markets' dependence structures with other financial markets. The Autoregressive-Generalized Autoregressive Conditional Heteroscedastic-t (AR-GARCH-t) model is used to examine the marginal distributions, while Normal and Generalized Joe-Clayton (GJC) copula models are employed to analyse 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 US 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, we find that the upper tail dependence is much higher than the lower tail dependence in some short periods, which has not been documented in previous literature. Third, Chinese financial market is relatively separate from other international financial markets in contrast to the US market. The tail dependence with other financial markets is much lower in China than in the United States. Dependence, on average, rises significantly over sub-periods.
