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A New Look at the Swing Contract: From Linear Programming to Particle Swarm Optimization

Author

Listed:
  • Tapio Behrndt

    (Gasum Oy, Revontulenpuisto 2C, 02100 Helsinki, Finland)

  • Ren-Raw Chen

    (Gabelli School Business, Fordham University, 45 Columbus Avenue, New York, NY 10019, USA)

Abstract

As the energy market has grown in importance in recent decades, researchers have paid increasing attention to swing option contracts. Early studies evaluated the swing contract as if it were a financial derivative contract, by ignoring its storage constraints. Aided by recent advances in artificial intelligence (AI) and machine learning (ML) technologies, recent studies were able to incorporate storage limitations. We make two discoveries in this paper. First, we contribute to the literature by proposing an AI methodology—particle swarm optimization (PSO)—for the evaluation of the swing contract. Compared to the other ML methodologies in the literature, PSO has an advantage by expanding to include more features. Secondly, we study the relative impact of the price process (exogenously given) that underlies the swing contract and the storage constraints that affect a quantity decision process (endogenously decided), and discover that the latter has a much greater impact than the former, indicating the limitation of the earlier literature that focused only on price dynamics.

Suggested Citation

  • Tapio Behrndt & Ren-Raw Chen, 2022. "A New Look at the Swing Contract: From Linear Programming to Particle Swarm Optimization," JRFM, MDPI, vol. 15(6), pages 1-20, May.
    • Handle: RePEc:gam:jjrfmx:v:15:y:2022:i:6:p:246-:d:828820
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    References listed on IDEAS

    as
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    2. Patrick Jaillet & Ehud I. Ronn & Stathis Tompaidis, 2004. "Valuation of Commodity-Based Swing Options," Management Science, INFORMS, vol. 50(7), pages 909-921, July.
    3. 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.
    4. Nicolas Curin & Michael Kettler & Xi Kleisinger-Yu & Vlatka Komaric & Thomas Krabichler & Josef Teichmann & Hanna Wutte, 2021. "A deep learning model for gas storage optimization," Papers 2102.01980, arXiv.org, revised Mar 2021.
    5. Roberto Daluiso & Emanuele Nastasi & Andrea Pallavicini & Giulio Sartorelli, 2020. "Pricing commodity swing options," Papers 2001.08906, arXiv.org.
    6. Ren-Raw Chen & Jeffrey Huang & William Huang & Robert Yu, 2021. "An Artificial Intelligence Approach to the Valuation of American-Style Derivatives: A Use of Particle Swarm Optimization," JRFM, MDPI, vol. 14(2), pages 1-22, February.
    7. Olivier Bardou & Sandrine Bouthemy & Gilles Pages, 2009. "Optimal Quantization for the Pricing of Swing Options," Applied Mathematical Finance, Taylor & Francis Journals, vol. 16(2), pages 183-217.
    Full references (including those not matched with items on IDEAS)

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