期货合约
波动性(金融)
可预测性
机器学习
经济
计算机科学
金融经济学
计量经济学
数学
统计
作者
Xinjie Lu,Feng Ma,Jin Xu,Zehui Zhang
标识
DOI:10.1016/j.irfa.2022.102299
摘要
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI