Forecasting the price of oil: A cautionary note

Thomas Conlon, John Cotter and Emmanuel Eyiah-Donkor

Journal of Commodity Markets, 2024, vol. 33, issue C

Abstract: We study the out-of-sample predictability of monthly crude oil prices using forecast combinations constructed from several individual predictor forecasts. Our empirical results indicate that combination forecasts of monthly average oil prices are more accurate than the no-change forecast with statistically significant reductions in mean square forecast errors (MSFE) and significant directional accuracy at every horizon up to 24 months, consistent with earlier evidence that forecast combinations greatly enhance the forecastability of oil prices. In contrast, we find no significant MSFE reductions or directional accuracy for forecasts of end-of-month oil prices at almost all horizons. Furthermore, we document that end-of-month forecasts when used to guide investment and hedging decisions of investors, statistically, do not deliver superior economic value to investors. Overall, the implication of our results is that the statistical and economic significance of forecasts of oil prices is heavily influenced by the construction of the underlying oil price series and provide a cautionary note on which oil price series to use in forecasting.

Keywords: Monthly average and end-of-month crude oil prices; Oil price predictability; Forecast combinations; Out-of-sample forecast performance; Investment and hedging decisions (search for similar items in EconPapers)
JEL-codes: C22 C32 C53 Q47 (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jocoma:v:33:y:2024:i:c:s2405851323000685

DOI: 10.1016/j.jcomm.2023.100378

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