  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.
