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

• This paper investigates the predictive ability of oil shocks for international stock market volatility based on a data-rich environment. • We further investigate the predictive ability of the best-performing oil shock by comparing its performance with that of traditional macroeconomic variables and uncertainty. • The LASSO method and regime-switching model can jointly deliver incremental improvement in forecasting accuracy. • This paper tries to provide new evidence for international stock market volatility prediction. 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. [ABSTRACT FROM AUTHOR]