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Forecasting crude oil price: Does exist an optimal econometric model?

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  • de Albuquerquemello, Vinícius Phillipe
  • de Medeiros, Rennan Kertlly
  • da Nóbrega Besarria, Cássio
  • Maia, Sinézio Fernandes

Abstract

The drastic reduction in oil prices after 2014 rekindled its stochastic characteristics of not settling around a mean and having unexpected high volatility. Thus, creating a branch of empirical literature devoted to the study of structural breaks in oil price longitudinal data, its treatment and forecasting. In that regard, this paper estimate and compare the accuracy measurements of different methodologies and propose the use of a Self-Exciting Threshold Auto-regressive - SETAR model. This approach automatically allows for regime switching after a threshold, hence achieving a Root Mean Square Error - RMSE of 2%, in contrast to 10% of other models commonly used. Moreover, the comparison with previous studies pointed out that the SETAR model surpasses most of the oil price prediction methods in relation to its accuracy, or because of its simplicity, since it does not require great computational effort or difficult analytical skills.

Suggested Citation

  • de Albuquerquemello, Vinícius Phillipe & de Medeiros, Rennan Kertlly & da Nóbrega Besarria, Cássio & Maia, Sinézio Fernandes, 2018. "Forecasting crude oil price: Does exist an optimal econometric model?," Energy, Elsevier, vol. 155(C), pages 578-591.
  • Handle: RePEc:eee:energy:v:155:y:2018:i:c:p:578-591
    DOI: 10.1016/j.energy.2018.04.187
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    Cited by:

    1. Carpio, Lucio Guido Tapia, 2019. "The effects of oil price volatility on ethanol, gasoline, and sugar price forecasts," Energy, Elsevier, vol. 181(C), pages 1012-1022.
    2. Wu, Binrong & Wang, Lin & Wang, Sirui & Zeng, Yu-Rong, 2021. "Forecasting the U.S. oil markets based on social media information during the COVID-19 pandemic," Energy, Elsevier, vol. 226(C).
    3. Akdoğan, Kurmaş, 2020. "Fundamentals versus speculation in oil market: The role of asymmetries in price adjustment?," Resources Policy, Elsevier, vol. 67(C).
    4. Rubaszek Michal & Karolak Zuzanna & Kwas Marek & Uddin Gazi Salah, 2020. "The role of the threshold effect for the dynamics of futures and spot prices of energy commodities," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 24(5), pages 1-20, December.
    5. Meng, Fanyi & Liu, Li, 2019. "Analyzing the economic sources of oil price volatility: An out-of-sample perspective," Energy, Elsevier, vol. 177(C), pages 476-486.
    6. Pavel Baboshkin & Mafura Uandykova, 2021. "Multi-source Model of Heterogeneous Data Analysis for Oil Price Forecasting," International Journal of Energy Economics and Policy, Econjournals, vol. 11(2), pages 384-391.
    7. 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).
    8. Butler, Sunil & Kokoszka, Piotr & Miao, Hong & Shang, Han Lin, 2021. "Neural network prediction of crude oil futures using B-splines," Energy Economics, Elsevier, vol. 94(C).

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    More about this item

    Keywords

    Forecasting; Oil prices; Accuracy; VAR; SETAR;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy

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