Encouraged by the literary fact that high-frequency data such as intraday returns contribute to estimating the tail risk of daily returns, we propose an intraday-return-based Value-at-Risk (VaR) model driven by dynamic conditional score with censored generalized Pareto distribution (hence, Censored GP-DCS-VaR model), which is a novel parametric VaR approach based on dynamic score-driven model and can incorporate intraday information into daily VaR forecast. This model helps present the dynamic evolution of intraday return distribution and well capture its tail feature. Applying bootstrap or a parametric method, we are allowed to form the daily return distribution in light of intraday data and thus can calculate VaR directly. Empirical analysis using the data of the Chinese stock market shows that our model gain an advantage in the risk estimation of extreme returns, proved by the comparison of out-of-sample forecasts between the Censored GP-DCS-VaR and the realized-GARCH-VaR.

受文学事实（例如日内收益之类的高频数据有助于估算日收益的尾部风险）的启发，我们提出了一种基于日内收益的风险价值（VaR）模型，该模型由动态条件得分驱动并带有广义广义Pareto检验分布（因此，审查了GP-DCS-VaR模型），这是一种基于动态评分驱动模型的新颖参数化VaR方法，可以将日内信息纳入每日VaR预测中。该模型有助于呈现日内收益分布的动态演变并很好地捕捉其尾部特征。应用自举或参数方法，我们可以根据日内数据形成日收益分布，从而可以直接计算VaR。