  A new realized conditional autoregressive Value-at-Risk (VaR) framework is
proposed, through incorporating a measurement equation into the original
quantile regression model. The framework is further extended by employing
various Expected Shortfall (ES) components, to jointly estimate and forecast
VaR and ES. The measurement equation models the contemporaneous dependence
between the realized measure (i.e., Realized Variance and Realized Range) and
the latent conditional ES. An adaptive Bayesian Markov Chain Monte Carlo method
is employed for estimation and forecasting, the properties of which are
assessed and compared with maximum likelihood through a simulation study. In a
comprehensive forecasting study on 1% and 2.5 % quantile levels, the proposed
models are compared to a range of parametric, non-parametric and
semi-parametric models, based on 7 market indices and 7 individual assets.
One-day-ahead VaR and ES forecasting results favor the proposed models,
especially when incorporating the sub-sampled Realized Variance and the
sub-sampled Realized Range in the model.
