Inaccurate Value at Risk Estimations: Bad Modeling or Inappropriate Data?

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).