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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.

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  • 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:

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    2. 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.
    3. 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.
    4. 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.
    5. 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.

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    More about this item

    Keywords

    Forecasting and prediction methods; Oil price forecast;

    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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