  This research incorporates realized volatility and overnight information into
risk models, wherein the overnight return often contributes significantly to
the total return volatility. Extending a semi-parametric regression model based
on asymmetric Laplace distribution, we propose a family of RES-CAViaR-oc models
by adding overnight return and realized measures as a nowcasting technique for
simultaneously forecasting Value-at-Risk (VaR) and expected shortfall (ES). We
utilize Bayesian methods to estimate unknown parameters and forecast VaR and ES
jointly for the proposed model family. We also conduct extensive backtests
based on joint elicitability of the pair of VaR and ES during the out-of sample
period. Our empirical study on four international stock indices confirms that
overnight return and realized volatility are vital in tail risk forecasting.
