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Uncertainty and fluctuation in crude oil price: evidence from machine learning models

Author

Listed:
  • Feng Ma

    (Southwest Jiaotong University)

  • Xinjie Lu

    (Southwest Jiaotong University)

  • Bo Zhu

    (Southwest Jiaotong University)

Abstract

This study comprehensively investigates the predictability of uncertainty indices for oil market volatility, employing multiple machine learning models based on a large set of uncertainty indices. Empirical findings demonstrate the efficiency of machine learning models for predicting oil futures volatility using uncertainty indices. The results are consistent across various robustness checks and special circumstances. This study highlights the need to combine the efficiency of machine learning models with as much information from uncertainty indices as possible to capture the dynamics of the oil market, which is essential for energy fields to confront future fierce situations and crises.

Suggested Citation

  • Feng Ma & Xinjie Lu & Bo Zhu, 2025. "Uncertainty and fluctuation in crude oil price: evidence from machine learning models," Annals of Operations Research, Springer, vol. 345(2), pages 725-755, February.
    • Handle: RePEc:spr:annopr:v:345:y:2025:i:2:d:10.1007_s10479-023-05463-7
    DOI: 10.1007/s10479-023-05463-7
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