  Under the framework of dynamic conditional score, we propose a parametric
forecasting model for Value-at-Risk based on the normal inverse Gaussian
distribution (Hereinafter NIG-DCS-VaR), which creatively incorporates intraday
information into daily VaR forecast. NIG specifies an appropriate distribution
to return and the semi-additivity of the NIG parameters makes it feasible to
improve the estimation of daily return in light of intraday return, and thus
the VaR can be explicitly obtained by calculating the quantile of the
re-estimated distribution of daily return. We conducted an empirical analysis
using two main indexes of the Chinese stock market, and a variety of
backtesting approaches as well as the model confidence set approach prove that
the VaR forecasts of NIG-DCS model generally gain an advantage over those of
realized GARCH (RGARCH) models. Especially when the risk level is relatively
high, NIG-DCS-VaR beats RGARCH-VaR in terms of coverage ability and
independence.
