Modelling Asymmetric Dependence of Financial Returns with Multivariate Dynamic Copulas

We propose a multidimensional extension for Patton's (2006) bivariate Dynamic Copulas. We also introduce a Dynamic Mixture Copula whose parameters and weights follow well defined dynamic processes. Both approaches are more flexible to adapt to financial data than currently available Copula models. We utilize the G7 stocks and bonds data to demonstrate the advantages of the proposed Dynamic Copulas. The object of interest is the analysis of the characteristics of financial market interactions. We apply the proposed dynamic models to demonstrate that neither stock nor bond market interactions are time-stable. Further, we conduct analyses to demonstrate that our suggested Dynamic Copulas are flexible enough to capture time-instable correlation patterns and to account for tail dependencies. Finally, we quantify the interaction characteristics of the G7 stocks and bonds markets and find that stocks tend to drop simultaneously during market turmoil. In contrast, bond markets offer diversification effects that tend to increase during market turbulences