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Regression analysis of historic oil prices: A basis for future mean reversion price scenarios

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  • Weijermars, R.
  • Sun, Z.

Abstract

We propose price forecasting algorithms based on regression analysis of historic oil prices over 150years (1861–2012). From 1986 onward daily market prices allow more detailed analyses of the principal crude oil benchmarks (West Texas Intermediate [WTI] and Brent). The mean reversion price for a given time period corresponds to the marginal cost of supply. When supply and demand are out of equilibrium, spot prices move in a bandwidth bound at the bottom by cash cost of supply and at the top by the concurrent price of demand destruction. Short-term elasticity of demand is 0.015 (highly inelastic), and long-term elasticity of supply changed from 0.99 (highly elastic) during 1965–1983 to 0.39 (less elastic) during 1984–2012. We derive functions for the long-term equilibrium price and expand them into scalable equilibrium price functions for forecasting future price scenarios if “business-as-usual” is assumed. We also consider how two hypothetical black swan events (“unknown unknowns”) may affect the mean equilibrium price.

Suggested Citation

  • Weijermars, R. & Sun, Z., 2018. "Regression analysis of historic oil prices: A basis for future mean reversion price scenarios," Global Finance Journal, Elsevier, vol. 35(C), pages 177-201.
    • Handle: RePEc:eee:glofin:v:35:y:2018:i:c:p:177-201
    DOI: 10.1016/j.gfj.2017.10.007
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    Cited by:

    1. Li, Jinchao & Guo, Yuwei, 2025. "A hybrid model based on iTransformer for risk warning of crude oil price fluctuations," Energy, Elsevier, vol. 314(C).
    2. Pedro Moreno & Isabel Figuerola-Ferretti & Antonio Muñoz, 2024. "Forecasting Oil Prices with Non-Linear Dynamic Regression Modeling," Energies, MDPI, vol. 17(9), pages 1-29, May.
    3. 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.
    4. 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.
    5. Michał Dominik Stasiak & Żaneta Staszak & Joanna Siwek & Dawid Wojcieszak, 2025. "Application of State Models in a Binary–Temporal Representation for the Prediction and Modelling of Crude Oil Prices," Energies, MDPI, vol. 18(3), pages 1-14, February.
    6. Ruud Weijermars & Arnaud Van Harmelen, 2018. "Shale Reservoir Drainage Visualized for a Wolfcamp Well (Midland Basin, West Texas, USA)," Energies, MDPI, vol. 11(7), pages 1-21, June.
    7. Xiaomei Yuan & Fang-Rong Ren & Tao-Feng Wu, 2025. "Oil Futures Dynamics and Energy Transition: Evidence from Macroeconomic and Energy Market Linkages," Energies, MDPI, vol. 18(14), pages 1-27, July.

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    Keywords

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    JEL classification:

    • Q11 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Aggregate Supply and Demand Analysis; Prices
    • Q31 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Nonrenewable Resources and Conservation - - - Demand and Supply; Prices
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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