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Forecasting Oil Prices: A Comparative Study

Author

Listed:
  • Jihad El Hokayem
  • Joseph Gemayel
  • Dany Mezher

Abstract

Oil prices have been a major concern for many policy makers, businesses and individuals throughout the years. The spillover of inflation, which is at its highest level since several decades, due to the supply chain problems and spike in energy prices, following the war between Russia and Ukraine pushed oil and gas under the spotlight recognizing its crucial role. In turn, this has imposed many challenges on numerous countries across regions building up feelings of fear and anxiety amid serious concerns about energy and food security. Forecasting oil prices is still a major challenge as it is stimulated by various influencers. With the availability of numerous techniques to forecast oil prices, this study aims to use multiple linear regressions which include the Vector Autoregression (VAR), Autoregressive Conditional Heteroskedasticity (ARCH) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models in order to attempt coming out with decent models that can help in forecasting oil prices and estimating their volatility.

Suggested Citation

  • Jihad El Hokayem & Joseph Gemayel & Dany Mezher, 2022. "Forecasting Oil Prices: A Comparative Study," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 14(7), pages 1-55, July.
    • Handle: RePEc:ibn:ijefaa:v:14:y:2022:i:7:p:55
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    References listed on IDEAS

    as
    1. Salisu, Afees A. & Fasanya, Ismail O., 2013. "Modelling oil price volatility with structural breaks," Energy Policy, Elsevier, vol. 52(C), pages 554-562.
    2. Wang, Yudong & Wu, Chongfeng, 2012. "Forecasting energy market volatility using GARCH models: Can multivariate models beat univariate models?," Energy Economics, Elsevier, vol. 34(6), pages 2167-2181.
    3. Zhao, Yang & Li, Jianping & Yu, Lean, 2017. "A deep learning ensemble approach for crude oil price forecasting," Energy Economics, Elsevier, vol. 66(C), pages 9-16.
    4. Zhang, Jin-Liang & Zhang, Yue-Jun & Zhang, Lu, 2015. "A novel hybrid method for crude oil price forecasting," Energy Economics, Elsevier, vol. 49(C), pages 649-659.
    5. Jaehyung An & Alexey Mikhaylov & Nikita Moiseev, 2019. "Oil Price Predictors: Machine Learning Approach," International Journal of Energy Economics and Policy, Econjournals, vol. 9(5), pages 1-6.
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    More about this item

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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