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

Yaojie Zhang, Yudong Wang and Feng Ma

Journal of Forecasting, 2021, vol. 40, issue 5, 733-768

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.

Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (13)

Downloads: (external link)
https://doi.org/10.1002/for.2737

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wly:jforec:v:40:y:2021:i:5:p:733-768

Access Statistics for this article

Journal of Forecasting is currently edited by Derek W. Bunn

More articles in Journal of Forecasting from John Wiley & Sons, Ltd.
Bibliographic data for series maintained by Wiley Content Delivery ().