Oil futures volatility predictability : new evidence based on machine learning models

This paper comprehensively investigates the connection between oil futures volatility and the financial market based on a data-rich and model-rich environment, which contains traditional prediction models, machine learning models, and combination models. The results highlight the efficiency of machine learning models for oil futures volatility forecasting, in particular ensemble models and neural network models. Most interestingly, we are the first to consider the “forecast combination puzzle” in machine learning models, and find that combination models continue to have more satisfactory performances in all types of situations. We also discuss the model interpretability and each indicator’s contribution to the prediction. Our paper provides new insights for machine learning methods’ applications in futures market volatility prediction, which is helpful for academics, policy-makers, portfolios and risk managers to anticipate risk in advance and efficiently take corresponding preventive measures