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.

翻译：本研究将已实现波动率和隔夜信息纳入风险模型，其中隔夜收益通常对总收益波动有显著贡献。基于非对称拉普拉斯分布扩展半参数回归模型，我们提出一类RES-CAViaR-oc模型，通过加入隔夜收益和已实现测度作为即时预测技术，用于同时预测风险价值（VaR）和期望损失（ES）。我们采用贝叶斯方法估计未知参数，并联合预测所提模型族的VaR和ES。此外，基于VaR和ES的联合可导出性，我们在样本外期间进行了广泛的反向测试。对四个国际股票指数的实证研究表明，隔夜收益和已实现波动率在尾部风险预测中至关重要。