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.
Relation:
International Journal of Finance and Economics, 28(3), Pp.3451-3466.
