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Forecasting crude oil prices: A reduced-rank approach

Author

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  • Song, Yixuan
  • He, Mengxi
  • Wang, Yudong
  • Zhang, Yaojie

Abstract

We use a reduced-rank approach (RRA) to forecast oil prices. The empirical results show that the RRA model outperforms the competitive models both in-sample and out-of-sample. We also find that the RRA model can generate economic value for investors. In addition, we explore the driving force of RRA's predictive power and show that the RRA model can effectively identify the predictive information of indicators, including magnitude and direction, and apply appropriate loadings to the predictors accordingly. Finally, our results are robust to multiple alternatives.

Suggested Citation

  • Song, Yixuan & He, Mengxi & Wang, Yudong & Zhang, Yaojie, 2023. "Forecasting crude oil prices: A reduced-rank approach," International Review of Economics & Finance, Elsevier, vol. 88(C), pages 698-711.
    • Handle: RePEc:eee:reveco:v:88:y:2023:i:c:p:698-711
    DOI: 10.1016/j.iref.2023.07.001
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Ouyang, Zisheng & Lu, Min & Ouyang, Zhongzhe & Zhou, Xuewei & Wang, Ren, 2024. "A novel integrated method for improving the forecasting accuracy of crude oil: ESMD-CFastICA-BiLSTM-Attention," Energy Economics, Elsevier, vol. 138(C).
    2. Bai, Yun & Deng, Shuyun & Pu, Ziqiang & Li, Chuan, 2024. "Carbon price forecasting using leaky integrator echo state networks with the framework of decomposition-reconstruction-integration," Energy, Elsevier, vol. 305(C).
    3. Gong, Xue & Lai, Ping & He, Mengxi & Wen, Danyan, 2024. "Climate risk and energy futures high frequency volatility prediction," Energy, Elsevier, vol. 307(C).
    4. Guan, Keqin & Gong, Xu, 2023. "A new hybrid deep learning model for monthly oil prices forecasting," Energy Economics, Elsevier, vol. 128(C).
    5. Yin, Linfei & Zheng, Da, 2024. "Decomposition prediction fractional-order PID reinforcement learning for short-term smart generation control of integrated energy systems," Applied Energy, Elsevier, vol. 355(C).
    6. Lahmiri, Salim, 2024. "Fossil energy market price prediction by using machine learning with optimal hyper-parameters: A comparative study," Resources Policy, Elsevier, vol. 92(C).

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    More about this item

    Keywords

    Forecasting; Crude oil; Reduced-rank approach; Technical indicators; Loadings;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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