Oil futures volatility predictability: New evidence based on machine learning models |
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| Institution: | 1. School of Economics and Management, Southwest Jiaotong University, Chengdu, China;2. Service Science and Innovation Key Laboratory of Sichuan Province, Chengdu, China;1. Department of Mathematics and School of Economics and Management, University of Bologna, Bologna, Italy;2. Department of Economics, Society and Politics, University of Urbino Carlo Bo, Italy;3. Department of Economics, University of Bamberg, Germany;1. John A Guthrie Endowed Chair in Banking and Financial Services at the College of Business, University of Wyoming, Laramie, WY, United States of America;2. Assistant Professor of Finance at the College of Business, University of Wyoming, Laramie, WY, United States of America;1. Middle East Technical University, Faculty of Economics and Administrative Sciences, Department of Business Administration, Dumlup?nar Bulvar?, Ankara 06800, Turkey;2. Bilkent University, Faculty of Business Administration, Department of Management, Bilkent, Ankara 06800, Turkey |
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| Abstract: | This paper comprehensively examines the connection between oil futures volatility and the financial market based on a model-rich environment, which contains traditional predicting models, machine learning models, and combination models. The results highlight the efficiency of machine learning models for oil futures volatility forecasting, particularly the ensemble models and neural network models. Most interestingly, we 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, and investors. |
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