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Volatility analysis and forecasting models of crude oil prices: a review

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  • Liwei Fan
  • Huiping Li

Abstract

Accurate forecasting of crude oil prices plays a significant role for supporting policy and decision making at economy and firm levels. The successive developments in econometric and artificial intelligence models provide opportunities to analyse crude oil market in depth and improve the accuracy of oil price forecasting. Past years have seen an increasing number of studies on the volatility analysis and forecasting of crude oil prices by different techniques such as econometric and artificial intelligence models. This paper aims to present a systematic review of existing tools used to model the volatility of crude oil prices. It is found that the integration of time series models with artificial intelligence models has received increasing attention in oil price forecasting owing to its satisfactory prediction performance. Also, feature extraction of oil price series with appropriate multivariate statistical analysis techniques plays an important role in improving the prediction performance.

Suggested Citation

  • Liwei Fan & Huiping Li, 2015. "Volatility analysis and forecasting models of crude oil prices: a review," International Journal of Global Energy Issues, Inderscience Enterprises Ltd, vol. 38(1/2/3), pages 5-17.
    • Handle: RePEc:ids:ijgeni:v:38:y:2015:i:1/2/3:p:5-17
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    Citations

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    Cited by:

    1. Antonio Ruiz-Porras & Javier Emmanuel Anguiano Pita, 2016. "Modelación de las dinámicas, volatilidades e interrelaciones de los rendimientos del petróleo mexicano, BRENT y WTI," Ensayos Revista de Economia, Universidad Autonoma de Nuevo Leon, Facultad de Economia, vol. 0(2), pages 175-194, November.
    2. Drachal, Krzysztof, 2021. "Forecasting selected energy commodities prices with Bayesian dynamic finite mixtures," Energy Economics, Elsevier, vol. 99(C).
    3. Potanin, Bogdan & Trifonov, Juri, 2021. "The influence of investors’ expectations on oil prices," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 63, pages 76-90.
    4. Arturo Lorenzo-Valdés, 2021. "Conditional Probability of Jumps in Oil Prices," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, vol. 16(4), pages 1-14, Octubre -.
    5. Krzysztof Drachal, 2018. "Determining Time-Varying Drivers of Spot Oil Price in a Dynamic Model Averaging Framework," Energies, MDPI, vol. 11(5), pages 1-24, May.
    6. V. V. Kossov, 2016. "A medium-term forecast of crude oil buyers’ prices," Studies on Russian Economic Development, Springer, vol. 27(6), pages 656-663, November.
    7. Yu, Hongchu & Fang, Zhixiang & Lu, Feng & Murray, Alan T. & Zhang, Hengcai & Peng, Peng & Mei, Qiang & Chen, Jinhai, 2019. "Impact of oil price fluctuations on tanker maritime network structure and traffic flow changes," Applied Energy, Elsevier, vol. 237(C), pages 390-403.
    8. Drachal, Krzysztof, 2018. "Comparison between Bayesian and information-theoretic model averaging: Fossil fuels prices example," Energy Economics, Elsevier, vol. 74(C), pages 208-251.
    9. 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.
    10. Shian-Chang Huang & Cheng-Feng Wu, 2018. "Energy Commodity Price Forecasting with Deep Multiple Kernel Learning," Energies, MDPI, vol. 11(11), pages 1-16, November.

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