Oil futures volatility prediction: Bagging or combination?

This paper compares the predictive performance of the bagging method and traditional combi-nation models for forecasting oil futures volatility, using economic policy uncertainty (EPU) indices and macroeconomic variables as predictors. Our empirical findings indicate that the bagging method outperforms the conventional combination models, demonstrating the effec-tiveness of machine learning combination models. These results are confirmed by different evaluation methods, alternative forecasting methods, and alternative oil futures, and hold up during the COVID-19 pandemic and various business cycles. Furthermore, we show that EPU indices are more useful than macroeconomic variables for forecasting oil volatility during the COVID-19 pandemic. Thus, our analysis provides new insights into combination forecasts.