Forecasting crude oil market volatility: A comprehensive look at uncertainty variables

Abstract

Uncertainty variables involving diverse aspects play leading roles in determining oil price movements. This study aims to improve the aggregate crude oil market volatility prediction based on a large set of uncertainty variables from a comprehensive viewpoint. Specifically, we apply three shrinkage methods, namely, forecast combination, dimension reduction, and variable selection, to extract valuable predictive information in a data-rich world. The empirical results show that the forecasting power of the individual uncertainty index is not satisfactory. By contrast, all shrinkage models, particularly the supervised machine learning techniques, demonstrate outstanding predictability of oil market volatility, which tends to be strong during business recessions. Notably, the sizeable economic gains confirm the superior forecasting performance of our comprehensive framework. We provide solid evidence that the two option-implied volatility variables uniformly serve as the best two predictors.