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A VAR-SVM model for crude oil price forecasting

Author

Listed:
  • Lutao Zhao
  • Lei Cheng
  • Yongtao Wan
  • Hao Zhang
  • Zhigang Zhang

Abstract

In recent years, the complexity and variability of international crude oil price have had an increasingly greater impact on society's economic development. Therefore, the accurate forecasting of crude oil price is helpful to maintain economic stability and avoid risks. This paper analyses the influencing factors, including market factors and non-market factors and then uses the Vector Autoregression (VAR) model to measure the relationship between oil price and those factors. Based on the results of VAR model, we put forward a new model - VAR-SVM, which is based on VAR and Support Vector Machine (SVM). Using VAR-SVM, we can make more accurate prediction of crude oil prices. Genetic Algorithm (GA) is employed to select model parameters. From the empirical results we can see that VAR-SVM model is superiority in accuracy and effectiveness comparing with the other forecasting models, such as VAR model, CGARCH model, Artificial Neural Network (ANN) model and autoregression SVM model.

Suggested Citation

  • Lutao Zhao & Lei Cheng & Yongtao Wan & Hao Zhang & Zhigang Zhang, 2015. "A VAR-SVM model for crude oil price forecasting," International Journal of Global Energy Issues, Inderscience Enterprises Ltd, vol. 38(1/2/3), pages 126-144.
    • Handle: RePEc:ids:ijgeni:v:38:y:2015:i:1/2/3:p:126-144
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    Citations

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    Cited by:

    1. Jianguo Zhou & Xuechao Yu & Xiaolei Yuan, 2018. "Predicting the Carbon Price Sequence in the Shenzhen Emissions Exchange Using a Multiscale Ensemble Forecasting Model Based on Ensemble Empirical Mode Decomposition," Energies, MDPI, vol. 11(7), pages 1-17, July.
    2. Manickavasagam, Jeevananthan & Visalakshmi, S. & Apergis, Nicholas, 2020. "A novel hybrid approach to forecast crude oil futures using intraday data," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
    3. Cheng, Fangzheng & Fan, Tijun & Fan, Dandan & Li, Shanling, 2018. "The prediction of oil price turning points with log-periodic power law and multi-population genetic algorithm," Energy Economics, Elsevier, vol. 72(C), pages 341-355.
    4. Krzysztof Drachal & Michał Pawłowski, 2021. "A Review of the Applications of Genetic Algorithms to Forecasting Prices of Commodities," Economies, MDPI, vol. 9(1), pages 1-22, January.
    5. Asit Kumar Das & Debahuti Mishra & Kaberi Das & Pradeep Kumar Mallick & Sachin Kumar & Mikhail Zymbler & Hesham El-Sayed, 2022. "Prophesying the Short-Term Dynamics of the Crude Oil Future Price by Adopting the Survival of the Fittest Principle of Improved Grey Optimization and Extreme Learning Machine," Mathematics, MDPI, vol. 10(7), pages 1-33, March.
    6. Parisa Foroutan & Salim Lahmiri, 2024. "Deep learning systems for forecasting the prices of crude oil and precious metals," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-40, December.
    7. Chai, Jian & Xing, Li-Min & Zhou, Xiao-Yang & Zhang, Zhe George & Li, Jie-Xun, 2018. "Forecasting the WTI crude oil price by a hybrid-refined method," Energy Economics, Elsevier, vol. 71(C), pages 114-127.
    8. Linlin Zhao & Jasper Mbachu & Zhansheng Liu, 2019. "Exploring the Trend of New Zealand Housing Prices to Support Sustainable Development," Sustainability, MDPI, vol. 11(9), pages 1-18, April.
    9. Wang, Delu & Ma, Gang & Song, Xuefeng & Liu, Yun, 2017. "Energy price slump and policy response in the coal-chemical industry district: A case study of Ordos with a system dynamics model," Energy Policy, Elsevier, vol. 104(C), pages 325-339.
    10. Fan, Liwei & Pan, Sijia & Li, Zimin & Li, Huiping, 2016. "An ICA-based support vector regression scheme for forecasting crude oil prices," Technological Forecasting and Social Change, Elsevier, vol. 112(C), pages 245-253.

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