Assessing the VaR of a portfolio using D-vine copula based multivariate GARCH models
نویسندگان
چکیده
We develop Bayesian inference of a multivariate GARCH model where the dependence is introduced by a D-vine copula on the innovations. A D-vine copula is a special case of vine copulas which are a relatively new and very flexible concept to construct multivariate copulas. In particular it allows to model dependency between pairs of margins individually. In a simulation study and three real data examples we assess the efficiency and bias of the widely used two step estimation approach. The flexibility of the model is demonstrated by considering a portfolio showing asymmetric dependencies between some pairs of assets and symmetric dependency between others. The benchmark (Fischer et al., 2009), the multivariate Student t copula, can not capture these characteristics. This lack of fit also affects the VaR.
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