Does the tail risk index matter in forecasting downside risk?
研究用增强型已实现GARCH模型检验SKEW和VVIX两个尾部风险指标能否提升标普500指数日VaR预测精度，发现VVIX能提供有价值信息，对风险管理和投资决策有帮助。
Abstract This study employs an augmented realized GARCH (RGARCH) model to examine whether two well‐known tail risk measures, namely the SKEW and VVIX indices, can improve the daily value‐at‐risk (VaR) forecasting accuracy for S&P500 index returns. We find that the RGARCH‐VVIX model exhibits better predictive accuracy than the RGARCH and RGARCH‐SKEW models. The VVIX index provides economically valuable information in forecasting VaR. Given its ability to improve both accuracy and efficiency for VaR forecasts, the RGARCH‐VVIX model is helpful for a risk manager to determine capital requirement and for investors to assess the downside risk of their investments.