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Merchant Commodity Storage and Term-Structure Model Error

Author

Listed:
  • Nicola Secomandi

    (Tepper School of Business, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213)

  • Guoming Lai

    (McCombs School of Business, University of Texas at Austin, Austin, Texas 78712)

  • François Margot

    (Tepper School of Business, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213)

  • Alan Scheller-Wolf

    (Tepper School of Business, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213)

  • Duane J. Seppi

    (Tepper School of Business, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213)

Abstract

Merchant operations involves valuing and hedging the cash flows of commodity- and energy-conversion assets as real options based on stochastic models that inevitably embed model error. In this paper we quantify how empirically calibrated model errors concerning the futures term structure affect the valuation and hedging of natural gas storage. We find that even small model errors—on the order of 1%–2% of the empirical futures price variance—can have a disproportionate impact on storage valuation and hedging. In particular, theoretically equivalent hedging strategies have very different sensitivities to model error, with one natural strategy exhibiting potentially catastrophic performance in the presence of small model errors. We propose effective approaches to mitigate the negative effect of futures term-structure model error on hedging, also taking into account futures contract illiquidity, and provide theoretical justification for some of these approaches. Beyond commodity storage, our analysis has relevance for other real and financial options that depend on futures term-structure dynamics, as well as for inventory, production, and capacity investment policies that rely on demand-forecast term structures.

Suggested Citation

  • Nicola Secomandi & Guoming Lai & François Margot & Alan Scheller-Wolf & Duane J. Seppi, 2015. "Merchant Commodity Storage and Term-Structure Model Error," Manufacturing & Service Operations Management, INFORMS, vol. 17(3), pages 302-320, July.
  • Handle: RePEc:inm:ormsom:v:17:y:2015:i:3:p:302-320
    DOI: 10.1287/msom.2015.0518
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    Cited by:

    1. Liao Wang & Jin Yao & Xiaowei Zhang, 2022. "How Does Risk Hedging Impact Operations? Insights from a Price-Setting Newsvendor Model," Papers 2201.01026, arXiv.org, revised Jun 2023.
    2. Anna Maria Gambaro & Nicola Secomandi, 2021. "A Discussion of Non‐Gaussian Price Processes for Energy and Commodity Operations," Production and Operations Management, Production and Operations Management Society, vol. 30(1), pages 47-67, January.
    3. Secomandi, Nicola, 2022. "Quadratic hedging of risk neutral values," Energy Economics, Elsevier, vol. 112(C).
    4. Yangfang (Helen) Zhou & Alan Scheller‐Wolf & Nicola Secomandi & Stephen Smith, 2019. "Managing Wind‐Based Electricity Generation in the Presence of Storage and Transmission Capacity," Production and Operations Management, Production and Operations Management Society, vol. 28(4), pages 970-989, April.
    5. Christian Mandl & Selvaprabu Nadarajah & Stefan Minner & Srinagesh Gavirneni, 2022. "Data‐driven storage operations: Cross‐commodity backtest and structured policies," Production and Operations Management, Production and Operations Management Society, vol. 31(6), pages 2438-2456, June.
    6. Secomandi, Nicola, 2016. "A tutorial on portfolio-based control algorithms for merchant energy trading operations," Journal of Commodity Markets, Elsevier, vol. 4(1), pages 1-13.
    7. Wu, Desheng & Olson, David L. & Wang, Shouyang, 2019. "Finance-operations interface mechanism and models," Omega, Elsevier, vol. 88(C), pages 1-3.
    8. Sripad K. Devalkar & Ravi Anupindi & Amitabh Sinha, 2018. "Dynamic Risk Management of Commodity Operations: Model and Analysis," Manufacturing & Service Operations Management, INFORMS, vol. 20(2), pages 317-332, May.
    9. James Cruise & Lisa Flatley & Richard Gibbens & Stan Zachary, 2019. "Control of Energy Storage with Market Impact: Lagrangian Approach and Horizons," Operations Research, INFORMS, vol. 67(1), pages 1-9, January.
    10. Selvaprabu Nadarajah & François Margot & Nicola Secomandi, 2015. "Relaxations of Approximate Linear Programs for the Real Option Management of Commodity Storage," Management Science, INFORMS, vol. 61(12), pages 3054-3076, December.
    11. Nicola Secomandi, 2020. "Quadratic Hedging and Optimization of Option Exercise Policies," Papers 2001.05788, arXiv.org, revised May 2022.
    12. Nadarajah, Selvaprabu & Margot, François & Secomandi, Nicola, 2017. "Comparison of least squares Monte Carlo methods with applications to energy real options," European Journal of Operational Research, Elsevier, vol. 256(1), pages 196-204.
    13. Nadarajah, Selvaprabu & Secomandi, Nicola, 2023. "A review of the operations literature on real options in energy," European Journal of Operational Research, Elsevier, vol. 309(2), pages 469-487.
    14. Sel, Burakhan & Minner, Stefan, 2022. "A hedging policy for seaborne forward freight markets based on probabilistic forecasts," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 166(C).

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