ABSTRACT The accurate forecasting of Value at Risk (VaR) and Expected Shortfall (ES) is crucial for investment decisions, external regulation, and risk capital allocation. Existing literature has demonstrated that both high‐frequency transaction data and economic policy uncertainty are important factors influencing volatility forecasts. Since tail risk measures are closely related to volatility, we propose fully extracting the information from 5‐min transaction data and economic policy uncertainty to model tail risk dynamics. We extract various factors from 5‐min transaction data that simulate volatility clustering, long memory, multi‐scale behavior, jumps, overnight information, and leverage effects, all of which drive risk dynamics. Additionally, we explore the role of monthly economic policy uncertainty factors, which reflect various aspects of the macroeconomy in tail risk estimation. However, structural breaks caused by business cycles, extreme events, and changes in economic policies may lead to changes in the explanatory power of the driving factors over time. To address the instability of the model, this paper proposes a mixed‐frequency time‐varying combination framework that assigns time‐varying weights to risk drivers of different frequencies to achieve forecast combination. The results from calibration tests, joint scoring functions, and Murphy diagrams consistently show that the proposed mixed‐frequency time‐varying combination framework outperforms existing models in terms of the accuracy and robustness of risk estimation.
Wang et al. (Sun,) studied this question.