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Hedging pressure momentum and the predictability of oil futures returns

Author

Listed:
  • Yu, Dan
  • Chen, Chuang
  • Wang, Yudong
  • Zhang, Yaojie

Abstract

In this paper, we distinguish the long- and short-term components of hedging pressure with the help of momentum rules and combine these components using the scaled principal component analysis (SPCA) to propose a hedging pressure momentum (HPM) index. Using data from January 1994 to June 2021, our empirical results indicate that the HPM index has a strong ability to predict oil futures returns with a significantly positive out-of-sample R2 of 0.946%. Moreover, the forecasting performance of HPM is higher than that of existing popular predictors. We find that the predictive power of our HPM index is partly derived from the channel of investor sentiment. Our findings on return predictability are robust under different settings that include various forecasting horizons, futures maturities, and multivariate information methods.

Suggested Citation

  • Yu, Dan & Chen, Chuang & Wang, Yudong & Zhang, Yaojie, 2023. "Hedging pressure momentum and the predictability of oil futures returns," Economic Modelling, Elsevier, vol. 121(C).
  • Handle: RePEc:eee:ecmode:v:121:y:2023:i:c:s0264999323000263
    DOI: 10.1016/j.econmod.2023.106214
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    Cited by:

    1. Shirui Wang & Tianyang Zhang, 2024. "Predictability of commodity futures returns with machine learning models," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 44(2), pages 302-322, February.
    2. Christina Sklibosios Nikitopoulos & Alice Carole Thomas & Jianxin Wang, 2024. "Hedging pressure and oil volatility: Insurance versus liquidity demands," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 44(2), pages 252-280, February.

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    More about this item

    Keywords

    Oil futures; Return predictability; Scaled principal component analysis; Hedging pressure momentum;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy

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