Semi-parametric Dynamic Asymmetric Laplace Models for Tail Risk Forecasting, Incorporating Realized Measures
  The joint Value at Risk (VaR) and expected shortfall (ES) quantile regression
model of Taylor (2017) 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 in Taylor (2017). The
comparison is in terms of accuracy of one-day-ahead Value-at-Risk and Expected
Shortfall 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.
