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Modeling and valuing make-up clauses in gas swing contracts

Author

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  • Edoli, Enrico
  • Fiorenzani, Stefano
  • Ravelli, Samuele
  • Vargiolu, Tiziano

Abstract

In the last 10years, thanks to the worldwide energy liberalization process, the birth of competitive gas markets and the recent financial crisis, traditional long term swing contracts in Europe have been supplemented in a significant way by make-up clauses which allow postponing the withdrawal of gas to future years when it could be more profitable. This introduces more complexity in the pricing and optimal management of swing contracts. This paper is devoted to a proper quantitative modelization of one type of make-up clause in a gas swing contract. More in detail, we succeed in building an algorithm to price and optimally manage the make-up gas allocation among the years and the gas taking in the swing subperiods within the years: we prove that this problem has a quadratic complexity with respect to the number of years. The algorithm can be adapted to different instances of make-up clauses as well as to some forms of carry-forward clauses. Then, as an example, we show the algorithm at work on a 3-year contract and we present a sensitivity analysis of the price and of the make-up policy with respect to various parameters relative both to the price dynamics and to the swing contract. To the authors' knowledge, this is the first time that such a quantitative treatment of make-up clauses appears in literature.

Suggested Citation

  • Edoli, Enrico & Fiorenzani, Stefano & Ravelli, Samuele & Vargiolu, Tiziano, 2013. "Modeling and valuing make-up clauses in gas swing contracts," Energy Economics, Elsevier, vol. 35(C), pages 58-73.
    • Handle: RePEc:eee:eneeco:v:35:y:2013:i:c:p:58-73
    DOI: 10.1016/j.eneco.2011.11.019
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    References listed on IDEAS

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    1. Eduardo Schwartz & James E. Smith, 2000. "Short-Term Variations and Long-Term Dynamics in Commodity Prices," Management Science, INFORMS, vol. 46(7), pages 893-911, July.
    2. Christophe Barrera-Esteve & Florent Bergeret & Charles Dossal & Emmanuel Gobet & Asma Meziou & Rémi Munos & Damien Reboul-Salze, 2006. "Numerical Methods for the Pricing of Swing Options: A Stochastic Control Approach," Methodology and Computing in Applied Probability, Springer, vol. 8(4), pages 517-540, December.
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    Cited by:

    1. Dong, Wenfeng & Kang, Boda, 2019. "Analysis of a multiple year gas sales agreement with make-up, carry-forward and indexation," Energy Economics, Elsevier, vol. 79(C), pages 76-96.
    2. Giorgia Callegaro & Luciano Campi & Valeria Giusto & Tiziano Vargiolu, 2017. "Utility indifference pricing and hedging for structured contracts in energy markets," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 85(2), pages 265-303, April.
    3. Marcus Eriksson & Jukka Lempa & Trygve Nilssen, 2014. "Swing options in commodity markets: a multidimensional Lévy diffusion model," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 79(1), pages 31-67, February.
    4. 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.
    5. M. Basei & A. Cesaroni & T. Vargiolu, 2013. "Optimal exercise of swing contracts in energy markets: an integral constrained stochastic optimal control problem," Papers 1307.1320, arXiv.org.

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    More about this item

    Keywords

    Swing option; Price decoupling; Make-up clause; Dynamic programming; Bang-bang controls;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • Q49 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Other

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