The illusion of oil return predictability: The choice of data matters!

Thomas Conlon, John Cotter and Emmanuel Eyiah-Donkor

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Abstract: Previous studies document statistically significant evidence of crude oil return predictability by several forecasting variables. We suggest that this evidence is misleading and follows from the common use of within-month averages of daily oil prices in calculating returns used in predictive regressions. Averaging introduces a bias in the estimates of the first-order autocorrelation coefficient and variance of returns. Consequently, estimates of regression coefficients are inefficient and associated t-statistics are overstated, leading to false inference about the true extent of in-sample and out-of-sample return predictability. On the contrary, using end-of-month data, we do not find convincing evidence for the predictability of oil returns. Our results highlight and provide a cautionary tale on how the choice of data could influence hypothesis testing for return predictability.

Keywords: Averaged crude oil prices; Spurious autocorrelation; Return predictability; Out-of-sample forecasts; Statistical inference (search for similar items in EconPapers)
Date: 2022-01
Note: View the original document on HAL open archive server: https://rennes-sb.hal.science/hal-03519860
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Citations: View citations in EconPapers (8)

Published in Journal of Banking and Finance, 2022, 134, pp.106331. ⟨10.1016/j.jbankfin.2021.106331⟩

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Journal Article: The illusion of oil return predictability: The choice of data matters! (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-03519860

DOI: 10.1016/j.jbankfin.2021.106331

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