Countries around the world have experienced severe-to-moderate economic restrictions during the first two waves of COVID-19 pandemic. The present article captures the time frame of this unprecedented turmoil to test the efficacy of the conditional extreme value theory (EVT) model to forecast value at risk (VaR) and expected shortfall (ES). The article considers the eight most-affected countries and applies the generalized autoregressive conditional heteroskedasticity (GARCH) process in appropriate GARCH-EVT models assuming Student’s t distribution to accommodate the fat-tailed behaviour of financial return series. The study confirms that our model is substantially superior in forecasting intraday VaR and ES in comparison to other conditional EVT models with a classical assumption of normal distribution as well as an unconditional EVT model. Contrary to more commonly used 5-minute-frequency data in intraday risk management, we use hourly data, which is much less researched in the context of stock market volatility forecasting. Moreover, our study adds to the reliability of conditional EVT models because of its accuracy in predicting intraday risk during extreme economic uncertainty in severely pandemic-hit countries.

在前两波 COVID-19 大流行期间，世界各国都经历了重度至中度的经济限制。本文捕捉了这场前所未有的动荡的时间框架，以测试条件极值理论 (EVT) 模型预测风险价值 (VaR) 和预期短缺 (ES) 的有效性。本文考虑了受影响最严重的八个国家，并在适当的 GARCH-EVT 模型中应用广义自回归条件异方差 (GARCH) 过程，假设学生的t分布以适应财务回报系列的肥尾行为。研究证实，与具有经典正态分布假设的其他条件 EVT 模型以及无条件 EVT 模型相比，我们的模型在预测日内 VaR 和 ES 方面具有显着优势。与盘中风险管理中更常用的 5 分钟频率数据相反，我们使用小时数据，这在股票市场波动预测的背景下研究较少。此外，我们的研究增加了条件 EVT 模型的可靠性，因为它可以准确地预测受疫情影响严重的国家的极端经济不确定性期间的盘中风险。