Forecasting accurate Value-at-Risk (VaR) estimations is a crucial task in applied financial risk management. Even though there have been significant advances in the field of financial econometrics, many crises have been documented throughout the world in the last decades. An explanation for this discrepancy is that many contemporary models are too complex and cannot be easily understood and implemented in the financial industry (Fama in Financ Anal J 51:75–80, 1995; Ross in AIMR conference proceedings, vol. 1993, no. 6, pp. 11–15, Association for Investment Management and Research, 1993). In order to bridge this theory–practice gap, we present a computational method based on the leverage effect. This method allows us to focus on financial theory and remove complexity. Examining the US stock market (2000–2020), we provide empirical evidence that our newly suggested approach, which uses only the most appropriate observation period, significantly increases the accuracy of the Conventional Delta Normal VaR model and generates VaR estimations which are as accurate as those of advanced econometric models, such as GARCH(1,1).

预测准确的风险价值 (VaR) 估计是应用金融风险管理中的一项关键任务。尽管金融计量经济学领域取得了重大进展，但在过去几十年中，世界各地仍记录了许多危机。对这种差异的一种解释是，许多当代模型过于复杂，在金融业中不易理解和实施（Fama in Financ Anal J 51:75-80, 1995；Ross in AIMR 会议论文集，vol. 1993，no. 6，第 11-15 页，投资管理与研究协会，1993 年）。为了弥合这一理论与实践的差距，我们提出了一种基于杠杆效应的计算方法。这种方法使我们能够专注于金融理论并消除复杂性。审视美国股市（2000-2020），