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.

涉及多个方面的不确定性变量在决定油价走势方面发挥着主导作用。本研究旨在从综合角度改进基于大量不确定变量的原油市场总体波动预测。具体来说，我们应用三种收缩方法，即预测组合、降维和变量选择，在数据丰富的世界中提取有价值的预测信息。实证结果表明，个体不确定性指数的预测能力并不理想。相比之下，所有收缩模型，尤其是监督机器学习技术，都表现出对石油市场波动的出色可预测性，而这种波动在商业衰退期间往往会很强。尤其，巨大的经济收益证实了我们综合框架的卓越预测性能。我们提供了确凿的证据，证明这两个期权隐含波动率变量一致地充当了最好的两个预测变量。