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A Two-Factor Cointegrated Commodity Price Model with an Application to Spread Option Pricing

Author

Listed:
  • Ciprian Necula

    (University of Zurich and Bucharest University of Economic Studies)

  • Elise Gourier

    (University of London)

  • Robert Huitema

    (University of Zurich)

  • Walter Farkas

    (University of Zurich, Ecole Polytechnique Fédérale de Lausanne, Swiss Finance Institute, and ETH Zürich)

Abstract

In this paper, we propose an easy-to-use yet comprehensive model for a system of cointegrated commodity prices. While retaining the exponential affine structure of previous approaches, our model allows for an arbitrary number of cointegration relationships. We show that the cointegration component allows capturing well-known features of commodity prices, i.e., upward sloping (contango) and downward sloping (backwardation) term-structures, smaller volatilities for longer maturities and an upward sloping correlation term structure. The model is calibrated to futures price data of ten commodities. The results provide compelling evidence of cointegration in the data. Implications for the prices of futures and options written on common commodity spreads (e.g., spark spread and crack spread) are thoroughly investigated.

Suggested Citation

  • Ciprian Necula & Elise Gourier & Robert Huitema & Walter Farkas, 2015. "A Two-Factor Cointegrated Commodity Price Model with an Application to Spread Option Pricing," Swiss Finance Institute Research Paper Series 15-54, Swiss Finance Institute, revised Jun 2016.
    • Handle: RePEc:chf:rpseri:rp1554
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    2. Deschatre, Thomas & Féron, Olivier & Gruet, Pierre, 2021. "A survey of electricity spot and futures price models for risk management applications," Energy Economics, Elsevier, vol. 102(C).
    3. Nielsen, Mikkel Slot, 2020. "On non-stationary solutions to MSDDEs: Representations and the cointegration space," Stochastic Processes and their Applications, Elsevier, vol. 130(5), pages 3154-3173.
    4. Fred Espen Benth & Marco Piccirilli & Tiziano Vargiolu, 2017. "Additive energy forward curves in a Heath-Jarrow-Morton framework," Papers 1709.03310, arXiv.org, revised Jun 2018.
    5. Thomas Deschatre & Olivier F'eron & Pierre Gruet, 2021. "A survey of electricity spot and futures price models for risk management applications," Papers 2103.16918, arXiv.org, revised Jul 2021.
    6. Farshid Mehrdoust & Idin Noorani, 2023. "Valuation of Spark-Spread Option Written on Electricity and Gas Forward Contracts Under Two-Factor Models with Non-Gaussian Lévy Processes," Computational Economics, Springer;Society for Computational Economics, vol. 61(2), pages 807-853, February.
    7. Wim Ackooij & Debora Daniela Escobar & Martin Glanzer & Georg Ch. Pflug, 2020. "Distributionally robust optimization with multiple time scales: valuation of a thermal power plant," Computational Management Science, Springer, vol. 17(3), pages 357-385, October.
    8. Daniel Leonhardt & Antony Ware & Rudi Zagst, 2017. "A Cointegrated Regime-Switching Model Approach with Jumps Applied to Natural Gas Futures Prices," Risks, MDPI, vol. 5(3), pages 1-19, September.
    9. Shailesh Rastogi & Chaitaly Athaley, 2019. "Volatility Integration in Spot, Futures and Options Markets: A Regulatory Perspective," JRFM, MDPI, vol. 12(2), pages 1-15, June.
    10. Alessio Trivella & Selvaprabu Nadarajah & Stein-Erik Fleten & Denis Mazieres & David Pisinger, 2021. "Managing Shutdown Decisions in Merchant Commodity and Energy Production: A Social Commerce Perspective," Manufacturing & Service Operations Management, INFORMS, vol. 23(2), pages 311-330, March.

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    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • 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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