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Forecasting the real prices of crude oil using robust regression models with regularization constraints

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  • Hao, Xianfeng
  • Zhao, Yuyang
  • Wang, Yudong

Abstract

In this paper, we forecast the real price of crude oil via a robust loss function (Huber), with regularization constraints including LASSO, Ridge, and Elastic Net. These modifications are designed to avoid problems with overfitting and improve out-of-sample predictive performance. The efficient implementation of penalized regression for Huber losses is supported by the accelerated proximal gradient algorithm. Our results indicate that equal-weight mean combinations based on robust parameter design and parameterization penalties can outperform the benchmark no-change model at all horizons (up to two years). We also find that combinations of forecasts from robust penalized models can significantly outperform those based on OLS in horizons of longer than three months. These models have consistent and significantly higher directional accuracy than the no-change model, with success ratios of up to 63.9%.

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  • Hao, Xianfeng & Zhao, Yuyang & Wang, Yudong, 2020. "Forecasting the real prices of crude oil using robust regression models with regularization constraints," Energy Economics, Elsevier, vol. 86(C).
  • Handle: RePEc:eee:eneeco:v:86:y:2020:i:c:s0140988320300220
    DOI: 10.1016/j.eneco.2020.104683
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    Cited by:

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    3. Xing, Li-Min & Zhang, Yue-Jun, 2022. "Forecasting crude oil prices with shrinkage methods: Can nonconvex penalty and Huber loss help?," Energy Economics, Elsevier, vol. 110(C).
    4. Wang, Yudong & Hao, Xianfeng, 2022. "Forecasting the real prices of crude oil: A robust weighted least squares approach," Energy Economics, Elsevier, vol. 116(C).
    5. Wen, Danyan & Liu, Li & Wang, Yudong & Zhang, Yaojie, 2022. "Forecasting crude oil market returns: Enhanced moving average technical indicators," Resources Policy, Elsevier, vol. 76(C).
    6. Zhang, Yaojie & He, Mengxi & Wen, Danyan & Wang, Yudong, 2023. "Forecasting crude oil price returns: Can nonlinearity help?," Energy, Elsevier, vol. 262(PB).
    7. Abdollahi, Hooman, 2020. "A novel hybrid model for forecasting crude oil price based on time series decomposition," Applied Energy, Elsevier, vol. 267(C).
    8. Mengxi He & Xianfeng Hao & Yaojie Zhang & Fanyi Meng, 2021. "Forecasting stock return volatility using a robust regression model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(8), pages 1463-1478, December.
    9. Afees A. Salisu & Rangan Gupta & Elie Bouri & Qiang Ji, 2022. "Mixed‐frequency forecasting of crude oil volatility based on the information content of global economic conditions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(1), pages 134-157, January.
    10. Zhang, Zhikai & He, Mengxi & Zhang, Yaojie & Wang, Yudong, 2022. "Geopolitical risk trends and crude oil price predictability," Energy, Elsevier, vol. 258(C).
    11. Fang, Tianhui & Zheng, Chunling & Wang, Donghua, 2023. "Forecasting the crude oil prices with an EMD-ISBM-FNN model," Energy, Elsevier, vol. 263(PA).
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