Forecasting the volatility of the German stock market: New evidence. Issue 9 (19th February 2022) - Liang, Chao; Zhang, Yi; Zhang, Yaojie
ABSTRACT: This study mainly explores whether the implied volatility indices of international stock markets and crude oil contain useful information in predicting the realized volatility (RV) of the German stock market. We use the standard predictive regression model, principal component analysis, five combination methods, and two shrinkage models to generate forecasts of DAX index volatility. First, the in-sample results indicate that almost all of the implied volatility indices considered have significant predictive power for the RV of the German DAX index. Second, the out-of-sample predictions suggest that the two shrinkage models exhibit the best out-of-sample predictions. Furthermore, a mean-variance investor can allocate portfolios through volatility predictions based on shrinkage models to achieve considerable economic gains. Finally, our conclusions are supported by numerous robustness checks.