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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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    as
    1. Ivo Welch & Amit Goyal, 2008. "A Comprehensive Look at The Empirical Performance of Equity Premium Prediction," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1455-1508, July.
    2. Baumeister, Christiane & Guérin, Pierre & Kilian, Lutz, 2015. "Do high-frequency financial data help forecast oil prices? The MIDAS touch at work," International Journal of Forecasting, Elsevier, vol. 31(2), pages 238-252.
    3. Markus K. Brunnermeier & Lasse Heje Pedersen, 2009. "Market Liquidity and Funding Liquidity," The Review of Financial Studies, Society for Financial Studies, vol. 22(6), pages 2201-2238, June.
    4. Hong, Harrison & Yogo, Motohiro, 2012. "What does futures market interest tell us about the macroeconomy and asset prices?," Journal of Financial Economics, Elsevier, vol. 105(3), pages 473-490.
    5. Wang, Yudong & Liu, Li & Wu, Chongfeng, 2017. "Forecasting the real prices of crude oil using forecast combinations over time-varying parameter models," Energy Economics, Elsevier, vol. 66(C), pages 337-348.
    6. Gao, Lei & Han, Yufeng & Zhengzi Li, Sophia & Zhou, Guofu, 2018. "Market intraday momentum," Journal of Financial Economics, Elsevier, vol. 129(2), pages 394-414.
    7. Zhang, Yaojie & Ma, Feng & Wang, Yudong, 2019. "Forecasting crude oil prices with a large set of predictors: Can LASSO select powerful predictors?," Journal of Empirical Finance, Elsevier, vol. 54(C), pages 97-117.
    8. Christiane Baumeister & Lutz Kilian, 2015. "Forecasting the Real Price of Oil in a Changing World: A Forecast Combination Approach," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(3), pages 338-351, July.
    9. Malcolm Baker & Jeffrey Wurgler, 2006. "Investor Sentiment and the Cross‐Section of Stock Returns," Journal of Finance, American Finance Association, vol. 61(4), pages 1645-1680, August.
    10. Frans A. De Roon & Theo E. Nijman & Chris Veld, 2000. "Hedging Pressure Effects in Futures Markets," Journal of Finance, American Finance Association, vol. 55(3), pages 1437-1456, June.
    11. Phan, Hoàng-Long & Zurbruegg, Ralf & Brockman, Paul & Yu, Chia-Feng (Jeffrey), 2022. "Time-to-maturity and commodity futures return volatility: The role of time-varying asymmetric information," Journal of Commodity Markets, Elsevier, vol. 26(C).
    12. He, Mengxi & Zhang, Yaojie & Wen, Danyan & Wang, Yudong, 2021. "Forecasting crude oil prices: A scaled PCA approach," Energy Economics, Elsevier, vol. 97(C).
    13. Wang, Yudong & Pan, Zhiyuan & Wu, Chongfeng & Wu, Wenfeng, 2020. "Industry equi-correlation: A powerful predictor of stock returns," Journal of Empirical Finance, Elsevier, vol. 59(C), pages 1-24.
    14. Deeney, Peter & Cummins, Mark & Dowling, Michael & Bermingham, Adam, 2015. "Sentiment in oil markets," International Review of Financial Analysis, Elsevier, vol. 39(C), pages 179-185.
    15. Li, Xuerong & Shang, Wei & Wang, Shouyang, 2019. "Text-based crude oil price forecasting: A deep learning approach," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1548-1560.
    16. Claeskens, Gerda & Magnus, Jan R. & Vasnev, Andrey L. & Wang, Wendun, 2016. "The forecast combination puzzle: A simple theoretical explanation," International Journal of Forecasting, Elsevier, vol. 32(3), pages 754-762.
    17. Lucey, Brian & Ren, Boru, 2021. "Does news tone help forecast oil?," Economic Modelling, Elsevier, vol. 104(C).
    18. Timmermann, Allan, 2006. "Forecast Combinations," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 4, pages 135-196, Elsevier.
    19. Yi, Yongsheng & Ma, Feng & Zhang, Yaojie & Huang, Dengshi, 2018. "Forecasting the prices of crude oil using the predictor, economic and combined constraints," Economic Modelling, Elsevier, vol. 75(C), pages 237-245.
    20. repec:bla:jfinan:v:53:y:1998:i:6:p:1839-1885 is not listed on IDEAS
    21. Lyu, Yongjian & Yi, Heling & Wei, Yu & Yang, Mo, 2021. "Revisiting the role of economic uncertainty in oil price fluctuations: Evidence from a new time-varying oil market model," Economic Modelling, Elsevier, vol. 103(C).
    22. Wenjin Kang & K. Geert Rouwenhorst & Ke Tang, 2020. "A Tale of Two Premiums: The Role of Hedgers and Speculators in Commodity Futures Markets," Journal of Finance, American Finance Association, vol. 75(1), pages 377-417, February.
    23. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
    24. Liu, Li & Bu, Ruijun & Pan, Zhiyuan & Xu, Yuhua, 2019. "Are financial returns really predictable out-of-sample?: Evidence from a new bootstrap test," Economic Modelling, Elsevier, vol. 81(C), pages 124-135.
    25. Capistrán, Carlos & Timmermann, Allan, 2009. "Forecast Combination With Entry and Exit of Experts," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 428-440.
    26. Lutz Kilian, 2009. "Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market," American Economic Review, American Economic Association, vol. 99(3), pages 1053-1069, June.
    27. Basu, Devraj & Miffre, Joëlle, 2013. "Capturing the risk premium of commodity futures: The role of hedging pressure," Journal of Banking & Finance, Elsevier, vol. 37(7), pages 2652-2664.
    28. Jonathan A. Batten & Harald Kinateder & Niklas Wagner, 2022. "Beating the Average: Equity Premium Variations, Uncertainty, and Liquidity," Abacus, Accounting Foundation, University of Sydney, vol. 58(3), pages 567-588, September.
    29. Symeonidis, Lazaros & Prokopczuk, Marcel & Brooks, Chris & Lazar, Emese, 2012. "Futures basis, inventory and commodity price volatility: An empirical analysis," Economic Modelling, Elsevier, vol. 29(6), pages 2651-2663.
    30. Harrison Hong & Jeremy C. Stein, 1999. "A Unified Theory of Underreaction, Momentum Trading, and Overreaction in Asset Markets," Journal of Finance, American Finance Association, vol. 54(6), pages 2143-2184, December.
    31. Ivar Ekeland & Delphine Lautier & Bertrand Villeneuve, 2019. "Hedging pressure and speculation in commodity markets," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 68(1), pages 83-123, July.
    32. John Y. Campbell & Samuel B. Thompson, 2008. "Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1509-1531, July.
    33. Batten, Jonathan A. & Kinateder, Harald & Szilagyi, Peter G. & Wagner, Niklas F., 2021. "Hedging stocks with oil," Energy Economics, Elsevier, vol. 93(C).
    34. Bessembinder, Hendrik & Chan, Kalok, 1992. "Time-varying risk premia and forecastable returns in futures markets," Journal of Financial Economics, Elsevier, vol. 32(2), pages 169-193, October.
    35. Yin, Libo & Yang, Qingyuan, 2016. "Predicting the oil prices: Do technical indicators help?," Energy Economics, Elsevier, vol. 56(C), pages 338-350.
    36. K. Geert Rouwenhorst & Ke Tang, 2012. "Commodity Investing," Annual Review of Financial Economics, Annual Reviews, vol. 4(1), pages 447-467, October.
    37. Amihud, Yakov, 2002. "Illiquidity and stock returns: cross-section and time-series effects," Journal of Financial Markets, Elsevier, vol. 5(1), pages 31-56, January.
    38. Acharya, Viral V. & Lochstoer, Lars A. & Ramadorai, Tarun, 2013. "Limits to arbitrage and hedging: Evidence from commodity markets," Journal of Financial Economics, Elsevier, vol. 109(2), pages 441-465.
    39. Batten, Jonathan A. & Kinateder, Harald & Szilagyi, Peter G. & Wagner, Niklas F., 2019. "Liquidity, surprise volume and return premia in the oil market," Energy Economics, Elsevier, vol. 77(C), pages 93-104.
    40. Ana‐Maria Fuertes & Joëlle Miffre & Adrian Fernandez‐Perez, 2015. "Commodity Strategies Based on Momentum, Term Structure, and Idiosyncratic Volatility," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 35(3), pages 274-297, March.
    41. Wen, Danyan & Wang, Yudong & Zhang, Yaojie, 2021. "Intraday return predictability in China’s crude oil futures market: New evidence from a unique trading mechanism," Economic Modelling, Elsevier, vol. 96(C), pages 209-219.
    42. Han, Liyan & Lv, Qiuna & Yin, Libo, 2017. "Can investor attention predict oil prices?," Energy Economics, Elsevier, vol. 66(C), pages 547-558.
    43. Michael Dewally & Louis H. Ederington & Chitru S. Fernando, 2013. "Determinants of Trader Profits in Commodity Futures Markets," The Review of Financial Studies, Society for Financial Studies, vol. 26(10), pages 2648-2683.
    44. Zhang, Yaojie & Ma, Feng & Shi, Benshan & Huang, Dengshi, 2018. "Forecasting the prices of crude oil: An iterated combination approach," Energy Economics, Elsevier, vol. 70(C), pages 472-483.
    45. Alturki, Sultan & Olson, Eric, 2022. "Oil sentiment and the U.S. inflation premium," Energy Economics, Elsevier, vol. 114(C).
    46. Mark W. Watson & James H. Stock, 2004. "Combination forecasts of output growth in a seven-country data set," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(6), pages 405-430.
    47. Bredin, Don & O'Sullivan, Conall & Spencer, Simon, 2021. "Forecasting WTI crude oil futures returns: Does the term structure help?," Energy Economics, Elsevier, vol. 100(C).
    48. Bessembinder, Hendrik, 1992. "Systematic Risk, Hedging Pressure, and Risk Premiums in Futures Markets," The Review of Financial Studies, Society for Financial Studies, vol. 5(4), pages 637-667.
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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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