IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i9p2182-d1387880.html
   My bibliography  Save this article

Forecasting Oil Prices with Non-Linear Dynamic Regression Modeling

Author

Listed:
  • Pedro Moreno

    (ICAI School of Engineering, Comillas Pontifical University, 28015 Madrid, Spain)

  • Isabel Figuerola-Ferretti

    (ICADE and Center for Low Carbon Hydrogen Studies, Comillas Pontifical University, 28015 Madrid, Spain)

  • Antonio Muñoz

    (Institute for Research in Technology (IIT), ICAI School of Engineering, Comillas Pontifical University, 28015 Madrid, Spain)

Abstract

The recent energy crisis has renewed interest in forecasting crude oil prices. This paper focuses on identifying the main drivers determining the evolution of crude oil prices and proposes a statistical learning forecasting algorithm based on regression analysis that can be used to generate future oil price scenarios. A combination of a generalized additive model with a linear transfer function with ARIMA noise is used to capture the existence of combinations of non-linear and linear relationships between selected input variables and the crude oil price. The results demonstrate that the physical market balance or fundamental is the most important metric in explaining the evolution of oil prices. The effect of the trading activity and volatility variables are significant under abnormal market conditions. We show that forecast accuracy under the proposed model supersedes benchmark specifications, including the futures prices and analysts’ forecasts. Four oil price scenarios are considered for expository purposes.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:9:p:2182-:d:1387880
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/9/2182/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/17/9/2182/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ron Alquist & Lutz Kilian, 2010. "What do we learn from the price of crude oil futures?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 539-573.
    2. 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.
    3. Andrea Coppola, 2008. "Forecasting oil price movements: Exploiting the information in the futures market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 28(1), pages 34-56, January.
    4. Christiane Baumeister & Dimitris Korobilis & Thomas K. Lee, 2022. "Energy Markets and Global Economic Conditions," The Review of Economics and Statistics, MIT Press, vol. 104(4), pages 828-844, October.
    5. Christiane Baumeister & Lutz Kilian, 2015. "Forecasting the Real Price of Oil in a Changing World: A Forecast Combination Approach," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(3), pages 338-351, July.
    6. Florin Aliu & Jiří Kučera & Simona Hašková, 2023. "Agricultural Commodities in the Context of the Russia-Ukraine War: Evidence from Corn, Wheat, Barley, and Sunflower Oil," Forecasting, MDPI, vol. 5(1), pages 1-23, March.
    7. Hamilton, James D. & Wu, Jing Cynthia, 2014. "Risk premia in crude oil futures prices," Journal of International Money and Finance, Elsevier, vol. 42(C), pages 9-37.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lang, Korbinian & Auer, Benjamin R., 2020. "The economic and financial properties of crude oil: A review," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    2. Wang, Yudong & Liu, Li & Diao, Xundi & Wu, Chongfeng, 2015. "Forecasting the real prices of crude oil under economic and statistical constraints," Energy Economics, Elsevier, vol. 51(C), pages 599-608.
    3. Naser, Hanan, 2016. "Estimating and forecasting the real prices of crude oil: A data rich model using a dynamic model averaging (DMA) approach," Energy Economics, Elsevier, vol. 56(C), pages 75-87.
    4. Baumeister, Christiane, 2021. "Measuring Market Expectations," CEPR Discussion Papers 16520, C.E.P.R. Discussion Papers.
    5. Degiannakis, Stavros & Filis, George, 2018. "Forecasting oil prices: High-frequency financial data are indeed useful," Energy Economics, Elsevier, vol. 76(C), pages 388-402.
    6. Nida Çakır Melek & Charles W. Calomiris & Harry Mamaysky, 2020. "Mining for Oil Forecasts," Research Working Paper RWP 20-20, Federal Reserve Bank of Kansas City.
    7. Baumeister, Christiane & Guérin, Pierre & Kilian, Lutz, 2015. "Do high-frequency financial data help forecast oil prices? The MIDAS touch at work," International Journal of Forecasting, Elsevier, vol. 31(2), pages 238-252.
    8. Valenti, Daniele & Bastianin, Andrea & Manera, Matteo, 2023. "A weekly structural VAR model of the US crude oil market," Energy Economics, Elsevier, vol. 121(C).
    9. Nicholas Apergis, 2023. "Forecasting energy prices: Quantile‐based risk models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(1), pages 17-33, January.
    10. Amor Aniss Benmoussa & Reinhard Ellwanger & Stephen Snudden, 2020. "The New Benchmark for Forecasts of the Real Price of Crude Oil," Staff Working Papers 20-39, Bank of Canada.
    11. Stavros Degiannakis & George Filis & Vipin Arora, 2018. "Oil Prices and Stock Markets: A Review of the Theory and Empirical Evidence," The Energy Journal, , vol. 39(5), pages 85-130, September.
    12. Remzi Uctum & Georges Prat, 2021. "Modeling ex-ante risk premia in the oil market," Post-Print hal-03513121, HAL.
    13. Phan, Dinh Hoang Bach & Narayan, Paresh Kumar & Gong, Qiang, 2021. "Terrorist attacks and oil prices: Hypothesis and empirical evidence," International Review of Financial Analysis, Elsevier, vol. 74(C).
    14. Ellwanger, Reinhard & Snudden, Stephen, 2023. "Forecasts of the real price of oil revisited: Do they beat the random walk?," Journal of Banking & Finance, Elsevier, vol. 154(C).
    15. Knut Are Aastveit & Jamie L. Cross & Herman K. van Dijk, 2023. "Quantifying Time-Varying Forecast Uncertainty and Risk for the Real Price of Oil," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 41(2), pages 523-537, April.
    16. Van Robays, Ine & Belu Mănescu, Cristiana, 2014. "Forecasting the Brent oil price: addressing time-variation in forecast performance," Working Paper Series 1735, European Central Bank.
    17. Degiannakis, Stavros & Filis, George, 2017. "Forecasting oil prices," MPRA Paper 77531, University Library of Munich, Germany.
    18. Hao, Xianfeng & Zhao, Yuyang & Wang, Yudong, 2020. "Forecasting the real prices of crude oil using robust regression models with regularization constraints," Energy Economics, Elsevier, vol. 86(C).
    19. Kilian, Lutz & Baumeister, Christiane, 2014. "A General Approach to Recovering Market Expectations from Futures Prices With an Application to Crude Oil," CEPR Discussion Papers 10162, C.E.P.R. Discussion Papers.
    20. Bredin, Don & O'Sullivan, Conall & Spencer, Simon, 2021. "Forecasting WTI crude oil futures returns: Does the term structure help?," Energy Economics, Elsevier, vol. 100(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:17:y:2024:i:9:p:2182-:d:1387880. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.