Abstract
This article proposes a novel copula-based approach to estimate CoVaR. Copula selection is based on multivariate extreme value theory rather than model fitting results. The Clayton copula as a limiting lower threshold copula is found to be able to provide a useful dependence structure for modelling the joint tail behaviour of financial returns. The out-of-sample testing of one-step quantile forecasts shows that the dynamic Clayton copula significantly outperforms the alternatives. It also shows the GJR-GARCH-Skew t model is a powerful competitor of the GPD-based models. Hence, the method provides a simple and effective way for estimating CoVaR and VaR.
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