Title: Semi-parametric dynamic asymmetric Laplace models for tail risk forecasting, incorporating realized measures
Authors: Richard Gerlach - University of Sydney (Australia)
Chao Wang - The University of Sydney (Australia)
Chao Wang - The University of Sydney (Australia) [presenting]
Abstract: The joint Value-at-Risk (VaR) and expected shortfall (ES) quantile regression model is extended, via incorporating a realized measure to drive the tail risk dynamics, as a potentially more efficient driver than daily returns. Further, a new model for the dynamics of the ES component is proposed and tested. Both a maximum likelihood and an adaptive Bayesian Markov Chain Monte Carlo method are employed for estimation, whose properties are compared in a simulation study; results favour the Bayesian approach, subsequently employed in a forecasting study of seven financial market indices. The proposed models are compared to a range of parametric, non-parametric and semi-parametric competitors, including GARCH, Realized GARCH, Extreme Value Theory method and the joint VaR and ES models, in terms of accuracy of one-day-ahead VaR and ES forecasts, over a long forecast sample period that includes the global financial crisis in 2007-2008. The results are favorable for the proposed models incorporating a realized measure, especially when employing the sub-sampled Realized Variance and the sub-sampled Realized Range.