Forecasting stock market volatility using commodity futures volatility information

Abstract

By incorporating volatility information from nineteen commodity futures prices, this paper compares the predictive ability of traditional individual AR-type and combination forecasting models versus model shrinkage methods in predicting US stock market volatility. Our empirical results show that the Lasso shrinkage method has significantly better out-of-sample forecasting performance in not only the individual models but also the combination approaches. In particular, the Lasso model with all predictors exhibits the best out-of-sample forecasting performance, suggesting that incorporating all commodity futures volatility information by the model shrinkage approach is an effective way for market participants and policy-makers to obtain accurate forecasts of US stock market volatility. Further analysis shows that the predictability evidence is substantially clearer during high volatility periods than in low volatility regimes. Finally, alternative evaluation periods further confirm the robustness of our results.