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Dynamic speculation and hedging in commodity futures markets with a stochastic convenience yield

Author

Listed:
  • Constantin Mellios

    (PRISM Sorbonne - Pôle de recherche interdisciplinaire en sciences du management - UP1 - Université Paris 1 Panthéon-Sorbonne)

  • Pierre Six

    (CAS - Centre Automatique et Systèmes - Mines Paris - PSL (École nationale supérieure des mines de Paris) - PSL - Université Paris Sciences et Lettres)

  • Anh Ngoc Lai

    (CREM - Centre de recherche en économie et management - UNICAEN - Université de Caen Normandie - NU - Normandie Université - UR - Université de Rennes - CNRS - Centre National de la Recherche Scientifique)

Abstract

The main objective of this paper is to address, in an a continuous-time framework, the issue of using storable commodity futures as vehicles for hedging purposes when, in particular, the convenience yield as well as the market prices of risk evolve randomly over time. Following the martingale route and by operating a suitable constant relative risk aversion utility function (CRRA) specific change of numéraire, we solve the investor's dynamic optimization program to obtain quasi analytical solutions for optimal demands, which can be expressed in terms of two discount bonds (traded and synthetic). Contrary to the existing literature, we explicitly derive the individual optimal proportions invested in the spot commodity, in a discount bond and in the futures contracts, which can be computed in a simple recursive way. We suggest various decompositions allowing an investor to assess the sensitivity of the optimal demands to the state variables and to specify the role played by each risky asset. Empirical evidence shows that the convenience yield has a strong impact on the speculation and hedging positions and the interaction among time-varying risk premia determines the magnitude and the sign of these positions.

Suggested Citation

  • Constantin Mellios & Pierre Six & Anh Ngoc Lai, 2016. "Dynamic speculation and hedging in commodity futures markets with a stochastic convenience yield," Post-Print halshs-01244560, HAL.
  • Handle: RePEc:hal:journl:halshs-01244560
    DOI: 10.1016/j.ejor.2015.10.045
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    Cited by:

    1. Zonggang Ma & Chaoqun Ma & Zhijian Wu, 2022. "Pricing commodity-linked bonds with stochastic convenience yield, interest rate and counterparty credit risk: application of Mellin transform methods," Review of Derivatives Research, Springer, vol. 25(1), pages 47-91, April.
    2. Huthaifa Sameeh Alqaralleh & Ahmad Al-Saraireh & Alessandra Canepa, 2021. "Energy Market Risk Management under Uncertainty: A VaR Based on Wavelet Approach," International Journal of Energy Economics and Policy, Econjournals, vol. 11(5), pages 130-137.
    3. Alexander, Carol & Deng, Jun & Zou, Bin, 2023. "Hedging with automatic liquidation and leverage selection on bitcoin futures," European Journal of Operational Research, Elsevier, vol. 306(1), pages 478-493.
    4. Ouzan, Samuel & Six, Pierre, 2025. "The demand for hedging of oil producers: A tale of risk and regret," European Journal of Operational Research, Elsevier, vol. 321(1), pages 330-343.
    5. Zhu, Xuehong & Zhang, Hongwei & Zhong, Meirui, 2017. "Volatility forecasting using high frequency data: The role of after-hours information and leverage effects," Resources Policy, Elsevier, vol. 54(C), pages 58-70.
    6. Ma, Zonggang & Ma, Chaoqun & Wu, Zhijian, 2020. "Closed-form analytical solutions for options on agricultural futures with seasonality and stochastic convenience yield," Chaos, Solitons & Fractals, Elsevier, vol. 137(C).
    7. Junhe Chen & Marcos Escobar-Anel, 2021. "Model uncertainty on commodity portfolios, the role of convenience yield," Annals of Finance, Springer, vol. 17(4), pages 501-528, December.
    8. Geng, Jiang-Bo & Chen, Fu-Rui & Ji, Qiang & Liu, Bing-Yue, 2021. "Network connectedness between natural gas markets, uncertainty and stock markets," Energy Economics, Elsevier, vol. 95(C).
    9. Oliver Borgards & Robert L. Czudaj, 2023. "Long‐short speculator sentiment in agricultural commodity markets," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(4), pages 3511-3528, October.
    10. Luca Vincenzo Ballestra & Christian Tezza, 2025. "A multi-factor model for improved commodity pricing: Calibration and an application to the oil market," Papers 2501.15596, arXiv.org.
    11. Jebabli, Ikram & Roubaud, David, 2018. "Time-varying efficiency in food and energy markets: Evidence and implications," Economic Modelling, Elsevier, vol. 70(C), pages 97-114.
    12. Ji, Qiang & Liu, Bing-Yue & Nehler, Henrik & Uddin, Gazi Salah, 2018. "Uncertainties and extreme risk spillover in the energy markets: A time-varying copula-based CoVaR approach," Energy Economics, Elsevier, vol. 76(C), pages 115-126.
    13. Zhang, Dayong & Broadstock, David C., 2020. "Global financial crisis and rising connectedness in the international commodity markets," International Review of Financial Analysis, Elsevier, vol. 68(C).

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