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The stochastic seasonal behavior of energy commodity convenience yields

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  • Mirantes, Andrés García
  • Población, Javier
  • Serna, Gregorio

Abstract

This paper contributes to the commodity pricing literature by consistently modeling the convenience yield with its empirically observed properties. Specifically, in this paper, we show how a four-factor model for the stochastic behavior of commodity prices, with two long- and short-term factors and two additional seasonal factors, may accommodate some of the most important empirically observed characteristics of commodity convenience yields, such as the mean reversion and stochastic seasonality. Based on this evidence, a theoretical model is presented and estimated to characterize the commodity convenience yield dynamics that are consistent with previous findings. We also show that commodity price seasonality is better estimated through convenience yields than through futures prices.

Suggested Citation

  • Mirantes, Andrés García & Población, Javier & Serna, Gregorio, 2013. "The stochastic seasonal behavior of energy commodity convenience yields," Energy Economics, Elsevier, vol. 40(C), pages 155-166.
    • Handle: RePEc:eee:eneeco:v:40:y:2013:i:c:p:155-166
    DOI: 10.1016/j.eneco.2013.06.011
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    References listed on IDEAS

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    9. Miltersen, Kristian R. & Schwartz, Eduardo S., 1998. "Pricing of Options on Commodity Futures with Stochastic Term Structures of Convenience Yields and Interest Rates," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 33(1), pages 33-59, March.
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    Citations

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    Cited by:

    1. Sévi, Benoît, 2015. "Explaining the convenience yield in the WTI crude oil market using realized volatility and jumps," Economic Modelling, Elsevier, vol. 44(C), pages 243-251.
    2. Almansour, Abdullah, 2016. "Convenience yield in commodity price modeling: A regime switching approach," Energy Economics, Elsevier, vol. 53(C), pages 238-247.
    3. Inchauspe, Julian & Li, Jun & Park, Jason, 2020. "Seasonal patterns of global oil consumption: Implications for long term energy policy," Journal of Policy Modeling, Elsevier, vol. 42(3), pages 536-556.
    4. In Choi, 2023. "Does climate change affect economic data?," Empirical Economics, Springer, vol. 64(6), pages 2939-2956, June.
    5. Anh Ngoc Lai & Constantin Mellios, 2016. "Valuation of commodity derivatives with an unobservable convenience yield," Post-Print halshs-01183166, HAL.
    6. Omura, Akihiro & Todorova, Neda & Li, Bin & Chung, Richard, 2015. "Convenience yield and inventory accessibility: Impact of regional market conditions," Resources Policy, Elsevier, vol. 44(C), pages 1-11.
    7. Andrés García-Mirantes & Beatriz Larraz & Javier Población, 2020. "A Proposal to Fix the Number of Factors on Modeling the Dynamics of Futures Contracts on Commodity Prices," Mathematics, MDPI, vol. 8(6), pages 1-13, June.
    8. Ewald, Christian-Oliver & Haugom, Erik & Lien, Gudbrand & Størdal, Ståle & Wu, Yuexiang, 2022. "Trading time seasonality in commodity futures: An opportunity for arbitrage in the natural gas and crude oil markets?," Energy Economics, Elsevier, vol. 115(C).

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    More about this item

    Keywords

    Stochastic calculus; Commodity prices; Convenience yield; Seasonality; Kalman filter;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing

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