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Pricing swing options with regime switching

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  • M. Wahab
  • Chi-Guhn Lee

Abstract

Gasoline price is highly volatile and exhibits Markov regime-switching process. In the electricity and the natural gas markets, “swing” options, which can provide some protection against day-to-day price fluctuations, are used to incorporate flexibility in delivering acquired energy. We propose a framework for pricing swing options for an underlying variable that follows a regime-switching process. We study the proposed framework in the gasoline industry for pricing swing options under price uncertainty by extracting the gasoline market information, estimating the parameters of the regime-switching process, and then presenting different numerical examples. Copyright Springer Science+Business Media, LLC 2011

Suggested Citation

  • M. Wahab & Chi-Guhn Lee, 2011. "Pricing swing options with regime switching," Annals of Operations Research, Springer, vol. 185(1), pages 139-160, May.
    • Handle: RePEc:spr:annopr:v:185:y:2011:i:1:p:139-160:10.1007/s10479-009-0599-z
    DOI: 10.1007/s10479-009-0599-z
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    Cited by:

    1. Kun Park & Ward Whitt, 2013. "Continuous-time Markov chain models to estimate the premium for extended hedge fund lockups," Annals of Operations Research, Springer, vol. 211(1), pages 357-379, December.
    2. Tiziano De Angelis & Yerkin Kitapbayev, 2014. "On the optimal exercise boundaries of swing put options," Papers 1407.6860, arXiv.org, revised Jan 2017.
    3. Roy H. Kwon & Jonathan Y. Li, 2016. "A stochastic semidefinite programming approach for bounds on option pricing under regime switching," Annals of Operations Research, Springer, vol. 237(1), pages 41-75, February.
    4. Hendrik Kohrs & Hermann Mühlichen & Benjamin R. Auer & Frank Schuhmacher, 2019. "Pricing and risk of swing contracts in natural gas markets," Review of Derivatives Research, Springer, vol. 22(1), pages 77-167, April.
    5. Tiziano De Angelis & Yerkin Kitapbayev, 2018. "On the Optimal Exercise Boundaries of Swing Put Options," Mathematics of Operations Research, INFORMS, vol. 43(1), pages 252-274, February.
    6. Kai Zheng & Weidong Xu & Xili Zhang, 2023. "Multivariate Regime Switching Model Estimation and Asset Allocation," Computational Economics, Springer;Society for Computational Economics, vol. 61(1), pages 165-196, January.
    7. J. Lars Kirkby & Shi-Jie Deng, 2019. "Swing Option Pricing By Dynamic Programming With B-Spline Density Projection," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 22(08), pages 1-53, December.
    8. Kim, Kyoung-Kuk & Lee, Chi-Guhn, 2012. "Evaluation and optimization of feed-in tariffs," Energy Policy, Elsevier, vol. 49(C), pages 192-203.
    9. Carl Chiarella & Les Clewlow & Boda Kang, 2016. "The Evaluation Of Multiple Year Gas Sales Agreement With Regime Switching," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 19(01), pages 1-25, February.
    10. Kai Zheng & Yuying Li & Weidong Xu, 2021. "Regime switching model estimation: spectral clustering hidden Markov model," Annals of Operations Research, Springer, vol. 303(1), pages 297-319, August.
    11. Trigeorgis, Lenos & Tsekrekos, Andrianos E., 2018. "Real Options in Operations Research: A Review," European Journal of Operational Research, Elsevier, vol. 270(1), pages 1-24.
    12. Roy Kwon & Jonathan Li, 2016. "A stochastic semidefinite programming approach for bounds on option pricing under regime switching," Annals of Operations Research, Springer, vol. 237(1), pages 41-75, February.
    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.

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