Value-at-Risk prediction by higher moment dynamics.

In this paper the prediction of Value-at-Risk by means of models accounting for higher moment dynamics is studied. We consider the GARCHDSK model, which allows for dynamic skewness and kurtosis, and compare its performance with that of several widely adopted models. The analysis is based on the study of sequences of (long and short) VaR violations, for which the hypotheses of absence of autocorrelation and of correct coverage rates are assessed. Both in-sample and out-of-sample results are investigated.