Forecasting Expected Shortfall with a Generalized Asymmetric Student-T Distribution

Financial returns typically display heavy tails and some skewness, and conditional variance models with these features often outperform more limited models. The dierence in performance may be espe- cially important in estimating quantities that depend on tail features, including risk measures such as the expected shortfall. Here, using a recent generalization of the asymmetric Student-t distribution to allow separate parameters to control skewness and the thickness of each tail, we t daily nancial returns and forecast expected shortfall for the S&P 500 index and a number of individual company stocks; the generalized distribution is used for the standardized innovations in a nonlinear, asymmetric GARCH-type model. The results provide empirical evidence for the usefulness of the generalized distribution in improving prediction of downside market risk of nancial assets.