International stock market volatility: A data-rich environment based on oil shocks

Xinjie Lu, Feng Ma, Tianyang Wang and Fenghua Wen

Journal of Economic Behavior & Organization, 2023, vol. 214, issue C, 184-215

Abstract: This paper investigates the predictive ability of oil shocks for international stock market volatility based on a data-rich environment. Our empirical analysis shows that multiple oil shock measures contain valuable information for predicting stock market volatility, in addition to traditional economic variables and uncertainty indices. Moreover, based on the group 7 countries, the least absolute shrinkage and selection operator method and regime-switching model jointly deliver incremental improvement in forecasting accuracy from both statistical and economic perspectives. These results are confirmed by robustness checks under different business cycles and market conditions, including the COVID-19 pandemic.

Keywords: Oil shock; International stock market volatility; Big data environment; Markov-regime switching; COVID-19 pandemic (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:jeborg:v:214:y:2023:i:c:p:184-215

DOI: 10.1016/j.jebo.2023.08.005

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