A Copula-Based Autoregressive Conditional Dependence Model of International Stock Markets

This paper investigates the level and development of cross-country stock market dependence using daily returns on stock indices. The use of copulas allows us to build exible models of the joint distribution of stock index returns. In particular, we apply univariate AR(p)-GARCH(1,1) models to the margins with possibly skewed and fat tailed return innovations, while modelling the dependence between markets using parametric families of copulas which offer various alternatives to the commonly assumed normal dependence structure. Moreover, the dependence across stock markets is allowed to vary over time through a GARCH-like autoregressive conditional copula model. Using synchronous daily returns on U.S., U.K., and French stock indices, we find strong evidence that the conditional dependence between pairs of each of these markets varies over time. All market pairs show high levels of dependence persistence. The performance of the copula-based approach is compared with Engle's (2002) dynamic conditional correlation model and found to be superior.