IDEAS home Printed from https://ideas.repec.org/a/spr/metcap/v8y2006i4d10.1007_s11009-006-0427-8.html
   My bibliography  Save this article

Numerical Methods for the Pricing of Swing Options: A Stochastic Control Approach

Author

Listed:
  • Christophe Barrera-Esteve

    (Gaz de France - Direction de la Recherche)

  • Florent Bergeret

    (Gaz de France)

  • Charles Dossal

    (Ecole Polytechnique, Centre de Mathématiques Appliquées)

  • Emmanuel Gobet

    (ENSIMAG - INP Grenoble - Laboratoire de Modélisation et Calcul)

  • Asma Meziou

    (Ecole Polytechnique, Centre de Mathématiques Appliquées)

  • Rémi Munos

    (Ecole Polytechnique, Centre de Mathématiques Appliquées)

  • Damien Reboul-Salze

    (Gaz de France)

Abstract

In the natural gas market, many derivative contracts have a large degree of flexibility. These are known as Swing or Take-Or-Pay options. They allow their owner to purchase gas daily, at a fixed price and according to a volume of their choice. Daily, monthly and/or annual constraints on the purchased volume are usually incorporated. Thus, the valuation of such contracts is related to a stochastic control problem, which we solve in this paper using new numerical methods. Firstly, we extend the Longstaff–Schwarz methodology (originally used for Bermuda options) to our case. Secondly, we propose two efficient parameterizations of the gas consumption, one is based on neural networks and the other on finite elements. It allows us to derive a local optimal consumption law using a stochastic gradient ascent. Numerical experiments illustrate the efficiency of these approaches. Furthermore, we show that the optimal purchase is of bang-bang type.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:metcap:v:8:y:2006:i:4:d:10.1007_s11009-006-0427-8
    DOI: 10.1007/s11009-006-0427-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11009-006-0427-8
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11009-006-0427-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Thompson, Andrew C., 1995. "Valuation of Path-Dependent Contingent Claims with Multiple Exercise Decisions over Time: The Case of Take-or-Pay," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 30(2), pages 271-293, June.
    2. Longstaff, Francis A & Schwartz, Eduardo S, 2001. "Valuing American Options by Simulation: A Simple Least-Squares Approach," University of California at Los Angeles, Anderson Graduate School of Management qt43n1k4jb, Anderson Graduate School of Management, UCLA.
    3. Longstaff, Francis A & Schwartz, Eduardo S, 2001. "Valuing American Options by Simulation: A Simple Least-Squares Approach," The Review of Financial Studies, Society for Financial Studies, vol. 14(1), pages 113-147.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    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. Nicolas Essis-Breton & Patrice Gaillardetz, 2020. "Fast Lower and Upper Estimates for the Price of Constrained Multiple Exercise American Options by Single Pass Lookahead Search and Nearest-Neighbor Martingale," Papers 2002.11258, arXiv.org.
    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. Liu, Xiaoran & Ronn, Ehud I., 2020. "Using the binomial model for the valuation of real options in computing optimal subsidies for Chinese renewable energy investments," Energy Economics, Elsevier, vol. 87(C).
    5. Pflug, Georg C. & Broussev, Nikola, 2009. "Electricity swing options: Behavioral models and pricing," European Journal of Operational Research, Elsevier, vol. 197(3), pages 1041-1050, September.
    6. Kovacevic, Raimund M. & Pflug, Georg Ch., 2014. "Electricity swing option pricing by stochastic bilevel optimization: A survey and new approaches," European Journal of Operational Research, Elsevier, vol. 237(2), pages 389-403.
    7. Vincent Lemaire & Gilles Pag`es & Christian Yeo, 2023. "Swing contract pricing: with and without Neural Networks," Papers 2306.03822, arXiv.org, revised Mar 2024.
    8. Kourouvakalis, Stylianos, 2008. "Méthodes numériques pour la valorisation d'options swings et autres problèmes sur les matières premières," Economics Thesis from University Paris Dauphine, Paris Dauphine University, number 123456789/116 edited by Geman, Hélyette.
    9. Work, James & Hauer, Grant & Luckert, M.K. (Marty), 2018. "What ethanol prices would induce growers to switch from agriculture to poplar in Alberta? A multiple options approach," Journal of Forest Economics, Elsevier, vol. 33(C), pages 51-62.
    10. Kathrin Glau & Ricardo Pachon & Christian Potz, 2019. "Speed-up credit exposure calculations for pricing and risk management," Papers 1912.01280, arXiv.org.
    11. Pringles, Rolando & Olsina, Fernando & Penizzotto, Franco, 2020. "Valuation of defer and relocation options in photovoltaic generation investments by a stochastic simulation-based method," Renewable Energy, Elsevier, vol. 151(C), pages 846-864.
    12. Gabriel J Power & Charli D. Tandja M. & Josée Bastien & Philippe Grégoire, 2015. "Measuring infrastructure investment option value," Journal of Risk Finance, Emerald Group Publishing, vol. 16(1), pages 49-72, January.
    13. O. Samimi & Z. Mardani & S. Sharafpour & F. Mehrdoust, 2017. "LSM Algorithm for Pricing American Option Under Heston–Hull–White’s Stochastic Volatility Model," Computational Economics, Springer;Society for Computational Economics, vol. 50(2), pages 173-187, August.
    14. Mo, Jian-Lei & Schleich, Joachim & Zhu, Lei & Fan, Ying, 2015. "Delaying the introduction of emissions trading systems—Implications for power plant investment and operation from a multi-stage decision model," Energy Economics, Elsevier, vol. 52(PB), pages 255-264.
    15. Song-Ping Zhu & Xin-Jiang He, 2018. "A hybrid computational approach for option pricing," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 5(03), pages 1-16, September.
    16. Xuemei Gao & Dongya Deng & Yue Shan, 2014. "Lattice Methods for Pricing American Strangles with Two-Dimensional Stochastic Volatility Models," Discrete Dynamics in Nature and Society, Hindawi, vol. 2014, pages 1-6, April.
    17. Marcelo G. Figueroa, 2006. "Pricing Multiple Interruptible-Swing Contracts," Birkbeck Working Papers in Economics and Finance 0606, Birkbeck, Department of Economics, Mathematics & Statistics.
    18. Abdullah Almansour and Margaret Insley, 2016. "The Impact of Stochastic Extraction Cost on the Value of an Exhaustible Resource: An Application to the Alberta Oil Sands," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2).
    19. Lars Stentoft, 2008. "American Option Pricing Using GARCH Models and the Normal Inverse Gaussian Distribution," Journal of Financial Econometrics, Oxford University Press, vol. 6(4), pages 540-582, Fall.
    20. Jungmin An & Dong-Kwan Kim & Jinyeong Lee & Sung-Kwan Joo, 2021. "Least Squares Monte Carlo Simulation-Based Decision-Making Method for Photovoltaic Investment in Korea," Sustainability, MDPI, vol. 13(19), pages 1-14, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:metcap:v:8:y:2006:i:4:d:10.1007_s11009-006-0427-8. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.