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Improving the Forecasting Accuracy of Crude Oil Prices

Author

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  • Xuluo Yin

    (The College of Finance and Statistics, Hunan University, Changsha 410006, China)

  • Jiangang Peng

    (The College of Finance and Statistics, Hunan University, Changsha 410006, China)

  • Tian Tang

    (The College of Finance and Statistics, Hunan University, Changsha 410006, China)

Abstract

Currently, oil is the key element of energy sustainability, and its prices and economy have a strong mutual influence. Modeling a good method to accurately predict oil prices over long future horizons is challenging and of great interest to investors and policymakers. This paper forecasts oil prices using many predictor variables with a new time-varying weight combination approach. In doing so, we first use five single-variable time-varying parameter models to predict crude oil prices separately. Second, every special model is assigned a time-varying weight by the new combination approach. Finally, the forecasting results of oil prices are calculated. The results show that the paper’s method is robust and performs well compared to random walk.

Suggested Citation

  • Xuluo Yin & Jiangang Peng & Tian Tang, 2018. "Improving the Forecasting Accuracy of Crude Oil Prices," Sustainability, MDPI, vol. 10(2), pages 1-9, February.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:2:p:454-:d:131091
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    References listed on IDEAS

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    Cited by:

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    2. Li Jingjing & Tang Ling & Li Ling, 2020. "The Co-Movements Between Crude Oil Price and Internet Concerns: Causality Analysis in the Frequency Domain," Journal of Systems Science and Information, De Gruyter, vol. 8(3), pages 224-239, June.
    3. Paweł Mielcarz & Dmytro Osiichuk & Jarosław Cymerski, 2020. "Algorithmic Sangfroid? The Decline of Sensitivity of Crude Oil Prices to News on Potentially Disruptive Terror Attacks and Political Unrest," Sustainability, MDPI, vol. 13(1), pages 1-24, December.
    4. Çiğdem YILMAZ & Nilgün ÇİL, 2018. "Markov Switching Autoregressive Model for WTI Crude Oil Price," EKOIST Journal of Econometrics and Statistics, Istanbul University, Faculty of Economics, vol. 14(28), pages 45-56, December.
    5. Urolagin, Siddhaling & Sharma, Nikhil & Datta, Tapan Kumar, 2021. "A combined architecture of multivariate LSTM with Mahalanobis and Z-Score transformations for oil price forecasting," Energy, Elsevier, vol. 231(C).
    6. Krzysztof Drachal, 2018. "Some Novel Bayesian Model Combination Schemes: An Application to Commodities Prices," Sustainability, MDPI, vol. 10(8), pages 1-27, August.
    7. Yu-Wei Chen & Chui-Yu Chiu & Mu-Chun Hsiao, 2021. "An Auxiliary Index for Reducing Brent Crude Investment Risk—Evaluating the Price Relationships between Brent Crude and Commodities," Sustainability, MDPI, vol. 13(9), pages 1-45, April.

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