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Forecasting oil commodity spot price in a data-rich environment

Author

Listed:
  • Sabri Boubaker

    (Métis Lab
    Vietnam National University
    Swansea University)

  • Zhenya Liu

    (Renmin University of China
    Renmin University of China
    Aix-Marseille University)

  • Yifan Zhang

    (Renmin University of China)

Abstract

Statistical properties that vary with time represent a challenge for time series forecasting. This paper proposes a change point-adaptive-RNN (CP-ADARNN) framework to predict crude oil prices with high-dimensional monthly variables. We first detect the structural breaks in predictors using the change point technique, and subsequently train a prediction model based on ADARNN. Using 310 economic series as exogenous factors from 1993 to 2021 to predict the monthly return on the WTI crude oil real price, CP-ADARNN outperforms competing benchmarks by 12.5% in terms of the root mean square error and achieves a correlation of 0.706 between predicted and actual returns. Furthermore, the superiority of CP-ADARNN is robust for Brent oil price as well as during the COVID-19 pandemic. The findings of this paper provide new insights for investors and researchers in the oil market.

Suggested Citation

  • Sabri Boubaker & Zhenya Liu & Yifan Zhang, 2025. "Forecasting oil commodity spot price in a data-rich environment," Annals of Operations Research, Springer, vol. 345(2), pages 685-702, February.
    • Handle: RePEc:spr:annopr:v:345:y:2025:i:2:d:10.1007_s10479-022-05004-8
    DOI: 10.1007/s10479-022-05004-8
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    More about this item

    Keywords

    Change point detection; Recursive neural network; Oil price prediction; COVID-19;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy

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