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Forecasts of the real price of oil revisited: Do they beat the random walk?

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  • Ellwanger, Reinhard
  • Snudden, Stephen

Abstract

In macroeconomic forecasting, the real price of oil is traditionally computed as the monthly average price of oil deflated by the price index. Consequently, the no-change forecast used to benchmark forecasts of the real price of crude oil is a monthly average price. We demonstrate that an alternative no-change forecast which reflects the random walk forecast from daily oil prices – the end-of-month price – is significantly more accurate in predicting the real price of oil up to one year ahead. We find that at the one-step-ahead prediction, all existing forecasts that outperform the monthly average no-change forecast perform worse than the end-of-month no-change forecast. The results call into question the usefulness of existing forecasting approaches for the real price of crude oil relative to naive forecasts.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:jbfina:v:154:y:2023:i:c:s0378426623001619
    DOI: 10.1016/j.jbankfin.2023.106962
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    Cited by:

    1. Iregui, Ana María & Núñez, Héctor M. & Otero, Jesús, 2025. "Testing the efficiency of oil price forecast revisions in times of COVID-19 and the Russia–Ukraine conflict," Journal of Commodity Markets, Elsevier, vol. 40(C).
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    3. Christiane Baumeister & Florian Huber & Thomas K. Lee & Francesco Ravazzolo, 2026. "Forecasting Natural Gas Prices in Real Time," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 41(2), pages 139-155, March.
    4. Markos Farag, Stephen Snudden, Greg Upton, 2024. "Can Futures Prices Predict the Real Price of Primary Commodities?," LCERPA Working Papers jc0145, Laurier Centre for Economic Research and Policy Analysis, revised 2024.
    5. Kruse-Becher, Robinson & Letixerant, Philip, 2025. "Oil price expectations in explosive phases," Energy Economics, Elsevier, vol. 152(C).
    6. Nonejad Nima, 2026. "Out-of-Sample Density Prediction of the End-of-Month Price of Crude Oil and the U.S. Economic Policy Uncertainty Index," Journal of Time Series Econometrics, De Gruyter, vol. 18(1), pages 1-47.
    7. Martin McCarthy, Stephen Snudden, 2024. "Forecasts of Period-Average Exchange Rates: New Insights from Real-Time Daily Data," LCERPA Working Papers jc0148, Laurier Centre for Economic Research and Policy Analysis, revised Oct 2024.
    8. Nima Nonejad, 2024. "Point forecasts of the price of crude oil: an attempt to “beat” the end-of-month random-walk benchmark," Empirical Economics, Springer, vol. 67(4), pages 1497-1539, October.
    9. Hilde C. Bjørnland & Nicolás Hardy & Dimitris Korobilis, 2026. "Forecasting Oil Prices Across the Distribution: A Quantile VAR Approach," Working Papers No 03/2026, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    10. Ma, Yong & Li, Shuaibing & Zhou, Mingtao, 2024. "Forecasting crude oil prices: Does global financial uncertainty matter?," International Review of Economics & Finance, Elsevier, vol. 96(PC).
    11. Cheng Zhang, 2026. "A Nontrivial Upper Bound on the Out-of-Sample $R^2$ in Return Forecasting," Papers 2602.07841, arXiv.org, revised Apr 2026.
    12. Li, Kaixin & Zhang, Zhikai & Wang, Yudong & Zhang, Yaojie, 2024. "Forecasting crude oil returns with oil-related industry ESG indices," Journal of Commodity Markets, Elsevier, vol. 36(C).
    13. Eric Benyo, Reinhard Ellwanger, Stephen Snudden, 2025. "A Reappraisal of Real-time Forecasts of the Real Price of Oil," LCERPA Working Papers jc0158, Laurier Centre for Economic Research and Policy Analysis, revised Jun 2025.
    14. Ding, Lili & Zhao, Haoran & Zhang, Rui, 2024. "Predicting multi-frequency crude oil price dynamics: Based on MIDAS and STL methods," Energy, Elsevier, vol. 313(C).
    15. Kruse-Becher, Robinson, 2025. "Let’s switch again! Testing for speculative oil price bubbles based on rotated market expectations," Finance Research Letters, Elsevier, vol. 78(C).
    16. Prest, Brian C. & Fell, Harrison & Gordon, Deborah & Conway, TJ, 2024. "Estimating the emissions reductions from supply-side fossil fuel interventions," Energy Economics, Elsevier, vol. 136(C).
    17. Xiaotao Zhang & Zihui Xia & Feng He & Jing Hao, 2025. "Forecasting crude oil prices with alternative data and a deep learning approach," Annals of Operations Research, Springer, vol. 345(2), pages 1165-1191, February.
    18. Quinlan Lee, Stephen Snudden, 2025. "Exact Mixed-Frequency Data Sampling (eMIDAS)," LCERPA Working Papers jc0157, Laurier Centre for Economic Research and Policy Analysis, revised Jun 2025.
    19. Martin McCarthy, Stephen Snudden, 2024. "Predictable by Construction: Assessing Forecast Directional Accuracy of Temporal Aggregates," LCERPA Working Papers jc0147, Laurier Centre for Economic Research and Policy Analysis, revised Oct 2024.
    20. Cheng Zhang, 2024. "Movement Prediction-Adjusted Naive Forecast: Is the Naive Baseline Unbeatable in Financial Time Series Forecasting?," Papers 2406.14469, arXiv.org, revised Oct 2025.
    21. Gurdip Bakshi & Xiaohui Gao & Zhaowei Zhang, 2024. "What Insights Do Short-Maturity (7DTE) Return Predictive Regressions Offer about Risk Preferences in the Oil Market?," Commodities, MDPI, vol. 3(2), pages 1-23, May.
    22. Reinhard Ellwanger, Stephen Snudden, Lenin Arango-Castillo, 2023. "Seize the Last Day: Period-End-Point Sampling for Forecasts of Temporally Aggregated Data," LCERPA Working Papers bm0142, Laurier Centre for Economic Research and Policy Analysis.
    23. Martin McCarthy & Stephen Snudden, 2025. "Forecasts of Period-average Exchange Rates: Insights from Real-time Daily Data," RBA Research Discussion Papers rdp2025-09, Reserve Bank of Australia.
    24. Benmoussa, Amor Aniss & Ellwanger, Reinhard & Snudden, Stephen, 2026. "Carpe diem: Can daily oil prices improve model-based forecasts of the real price of crude oil?," International Journal of Forecasting, Elsevier, vol. 42(1), pages 281-295.
    25. Thomas Hagedorn & Till Kösters & Jan Wessel & Sebastian Specht, 2023. "No Need for Speed: Fuel Prices, Driving Speeds, and the Revealed Value of Time on the German Autobahn," Working Papers 39, Institute of Transport Economics, University of Muenster.

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    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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