Title: Semi-parametric dynamic asymmetric Laplace models for tail risk forecasting, incorporating realized measures
Authors: Richard Gerlach - University of Sydney (Australia) [presenting]
Chao Wang - The University of Sydney (Australia)
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. Both a maximum likelihood and an adaptive Bayesian Markov chain Monte Carlo method are employed for estimation, whose properties are assessed and compared via a simulation study; results favour the Bayesian approach, which is subsequently employed in a forecasting study of seven market indices and two individual assets. The proposed models are compared to a range of parametric, non-parametric and semi-parametric models, including GARCH, realized-GARCH and the joint VaR and ES quantile regression models. The comparison is 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 favor the proposed models incorporating a realized measure, especially when employing the sub-sampled realized variance and the sub-sampled realized range.