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Predicting commodity returns: Time series vs. cross sectional prediction models

Author

Listed:
  • Angelidis, Timotheos
  • Sakkas, Athanasios
  • Tessaromatis, Nikolaos

Abstract

Commodity cross-sectional models based on the commodity momentum, basis, and basis-momentum factors generate superior time-series and cross-sectional commodity return forecasts compared to the historical average and time-series forecasting models that use financial, macroeconomic, and commodity-specific variables as predictors. Timing and long-short strategies based on the commodity premium forecasts from cross-sectional models achieve significant utility gains compared to strategies based on the historical average or time series predictive models’ forecasts. Our evidence is robust across many commodities and different forecasting methodologies.

Suggested Citation

  • Angelidis, Timotheos & Sakkas, Athanasios & Tessaromatis, Nikolaos, 2025. "Predicting commodity returns: Time series vs. cross sectional prediction models," Journal of Commodity Markets, Elsevier, vol. 38(C).
    • Handle: RePEc:eee:jocoma:v:38:y:2025:i:c:s2405851325000194
    DOI: 10.1016/j.jcomm.2025.100475
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    More about this item

    Keywords

    Commodities; Factor premia; Commodity return predictability;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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