Forecasting US stock market volatility: How to use international volatility information

Abstract

Abstract This paper aims to accurately forecast US stock market volatility by using international market volatility information flows. The results show the significant ability of the combined international volatility information to predict US stock volatility. The predictability is found to be both statistically and economically significant. Furthermore, in this framework, we compare the performance of a large set of approaches dealing with multivariate information. Dynamic model averaging (DMA) and dynamic model selection (DMS) perform better than a wide variety of competing strategies, including the heterogeneous autoregressive (HAR) benchmark, kitchen sink model, popular forecast combinations, principal component analysis (PCA), partial least squares (PLS), and the ridge, lasso, and elastic net shrinkage methods. A wide range of extensions and robustness checks reduce the concern regarding data mining. DMA and DMS are also able to significantly forecast international stock market volatilities.

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