IDEAS home Printed from https://ideas.repec.org/a/eee/eneeco/v40y2013icp976-988.html
   My bibliography  Save this article

A model for hedging load and price risk in the Texas electricity market

Author

Listed:
  • Coulon, Michael
  • Powell, Warren B.
  • Sircar, Ronnie

Abstract

Energy companies with commitments to meet customers' daily electricity demands face the problem of hedging load and price risk. We propose a joint model for load and price dynamics, which is motivated by the goal of facilitating optimal hedging decisions, while also intuitively capturing the key features of the electricity market. Driven by three stochastic factors including the load process, our power price model allows for the calculation of closed-form pricing formulas for forwards and some options, products often used for hedging purposes. Making use of these results, we illustrate in a simple example the hedging benefit of these instruments, while also evaluating the performance of the model when fitted to the Texas electricity market.

Suggested Citation

  • Coulon, Michael & Powell, Warren B. & Sircar, Ronnie, 2013. "A model for hedging load and price risk in the Texas electricity market," Energy Economics, Elsevier, vol. 40(C), pages 976-988.
    • Handle: RePEc:eee:eneeco:v:40:y:2013:i:c:p:976-988
    DOI: 10.1016/j.eneco.2013.05.020
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0140988313001084
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.eneco.2013.05.020?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. Weron, R & Bierbrauer, M & Trück, S, 2004. "Modeling electricity prices: jump diffusion and regime switching," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 336(1), pages 39-48.
    2. Markus Burger & Bernhard Klar & Alfred Muller & Gero Schindlmayr, 2004. "A spot market model for pricing derivatives in electricity markets," Quantitative Finance, Taylor & Francis Journals, vol. 4(1), pages 109-122.
    3. Cartea, Álvaro & Villaplana, Pablo, 2008. "Spot price modeling and the valuation of electricity forward contracts: The role of demand and capacity," Journal of Banking & Finance, Elsevier, vol. 32(12), pages 2502-2519, December.
    4. René Aïd & Luciano Campi & Adrien Nguyen Huu & Nizar Touzi, 2009. "A Structural Risk-Neutral Model Of Electricity Prices," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 12(07), pages 925-947.
    5. Pirrong, Craig & Jermakyan, Martin, 2008. "The price of power: The valuation of power and weather derivatives," Journal of Banking & Finance, Elsevier, vol. 32(12), pages 2520-2529, December.
    6. Helyette Geman & A. Roncoroni, 2006. "Understanding the Fine Structure of Electricity Prices," Post-Print halshs-00144198, HAL.
    7. Alvaro Cartea & Marcelo Figueroa & Helyette Geman, 2009. "Modelling Electricity Prices with Forward Looking Capacity Constraints," Applied Mathematical Finance, Taylor & Francis Journals, vol. 16(2), pages 103-122.
    8. Geske, Robert, 1979. "The valuation of compound options," Journal of Financial Economics, Elsevier, vol. 7(1), pages 63-81, March.
    9. repec:dau:papers:123456789/11500 is not listed on IDEAS
    10. Alvaro Cartea & Marcelo Figueroa, 2005. "Pricing in Electricity Markets: A Mean Reverting Jump Diffusion Model with Seasonality," Applied Mathematical Finance, Taylor & Francis Journals, vol. 12(4), pages 313-335.
    11. Schwartz, Eduardo S, 1997. "The Stochastic Behavior of Commodity Prices: Implications for Valuation and Hedging," Journal of Finance, American Finance Association, vol. 52(3), pages 923-973, July.
    12. M. T. Barlow, 2002. "A Diffusion Model For Electricity Prices," Mathematical Finance, Wiley Blackwell, vol. 12(4), pages 287-298, October.
    13. Kim, Jae Ho & Powell, Warren B., 2011. "An hour-ahead prediction model for heavy-tailed spot prices," Energy Economics, Elsevier, vol. 33(6), pages 1252-1266.
    14. Ben Hambly & Sam Howison & Tino Kluge, 2009. "Modelling spikes and pricing swing options in electricity markets," Quantitative Finance, Taylor & Francis Journals, vol. 9(8), pages 937-949.
    15. Hull, John & White, Alan, 1990. "Pricing Interest-Rate-Derivative Securities," The Review of Financial Studies, Society for Financial Studies, vol. 3(4), pages 573-592.
    16. repec:dau:papers:123456789/1433 is not listed on IDEAS
    17. Hélyette Geman & Andrea Roncoroni, 2006. "Understanding the Fine Structure of Electricity Prices," The Journal of Business, University of Chicago Press, vol. 79(3), pages 1225-1262, May.
    18. Lyle, Matthew R. & Elliott, Robert J., 2009. "A 'simple' hybrid model for power derivatives," Energy Economics, Elsevier, vol. 31(5), pages 757-767, September.
    19. Rene Carmona & Michael Coulon & Daniel Schwarz, 2012. "Electricity price modeling and asset valuation: a multi-fuel structural approach," Papers 1205.2299, arXiv.org.
    20. Almut E. D. Veraart & Luitgard A. M. Veraart, 2012. "Modelling electricity day–ahead prices by multivariate Lévy semistationary processes," CREATES Research Papers 2012-13, Department of Economics and Business Economics, Aarhus University.
    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. Deschatre, Thomas & Féron, Olivier & Gruet, Pierre, 2021. "A survey of electricity spot and futures price models for risk management applications," Energy Economics, Elsevier, vol. 102(C).
    2. Andreas Wagner, 2014. "Residual Demand Modeling and Application to Electricity Pricing," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2).
    3. Rene Carmona & Michael Coulon & Daniel Schwarz, 2012. "Electricity price modeling and asset valuation: a multi-fuel structural approach," Papers 1205.2299, arXiv.org.
    4. Weron, Rafał, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1030-1081.
    5. Hain, Martin & Schermeyer, Hans & Uhrig-Homburg, Marliese & Fichtner, Wolf, 2017. "An Electricity Price Modeling Framework for Renewable-Dominant Markets," Working Paper Series in Production and Energy 23, Karlsruhe Institute of Technology (KIT), Institute for Industrial Production (IIP).
    6. Thomas Deschatre & Olivier F'eron & Pierre Gruet, 2021. "A survey of electricity spot and futures price models for risk management applications," Papers 2103.16918, arXiv.org, revised Jul 2021.
    7. Markus Hess, 2020. "Pricing electricity forwards under future information on the stochastic mean-reversion level," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 43(2), pages 751-767, December.
    8. René Aïd & Luciano Campi & Nicolas Langrené, 2010. "A structural risk-neutral model for pricing and hedging power derivatives," Working Papers hal-00525800, HAL.
    9. Rafal Weron, 2006. "Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach," HSC Books, Hugo Steinhaus Center, Wroclaw University of Science and Technology, number hsbook0601, December.
    10. Füss, Roland & Mahringer, Steffen & Prokopczuk, Marcel, 2013. "Electricity Spot and Derivatives Pricing when Markets are Interconnected," Working Papers on Finance 1323, University of St. Gallen, School of Finance.
    11. Fiuza de Bragança, Gabriel Godofredo & Daglish, Toby, 2016. "Can market power in the electricity spot market translate into market power in the hedge market?," Energy Economics, Elsevier, vol. 58(C), pages 11-26.
    12. Hain, Martin & Kargus, Tobias & Schermeyer, Hans & Uhrig-Homburg, Marliese & Fichtner, Wolf, 2022. "An electricity price modeling framework for renewable-dominant markets," Working Paper Series in Production and Energy 66, Karlsruhe Institute of Technology (KIT), Institute for Industrial Production (IIP).
    13. Cartea, Álvaro & González-Pedraz, Carlos, 2012. "How much should we pay for interconnecting electricity markets? A real options approach," Energy Economics, Elsevier, vol. 34(1), pages 14-30.
    14. Cartea, Álvaro & Villaplana, Pablo, 2008. "Spot price modeling and the valuation of electricity forward contracts: The role of demand and capacity," Journal of Banking & Finance, Elsevier, vol. 32(12), pages 2502-2519, December.
    15. Füss, Roland & Mahringer, Steffen & Prokopczuk, Marcel, 2015. "Electricity derivatives pricing with forward-looking information," Journal of Economic Dynamics and Control, Elsevier, vol. 58(C), pages 34-57.
    16. Janczura, Joanna & Trück, Stefan & Weron, Rafał & Wolff, Rodney C., 2013. "Identifying spikes and seasonal components in electricity spot price data: A guide to robust modeling," Energy Economics, Elsevier, vol. 38(C), pages 96-110.
    17. Fred Espen Benth & Jūratė Šaltytė Benth & Steen Koekebakker, 2008. "Stochastic Modeling of Electricity and Related Markets," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 6811, October.
    18. Hain, Martin & Schermeyer, Hans & Uhrig-Homburg, Marliese & Fichtner, Wolf, 2018. "Managing renewable energy production risk," Journal of Banking & Finance, Elsevier, vol. 97(C), pages 1-19.
    19. N. K. Nomikos & O. Soldatos, 2008. "Using Affine Jump Diffusion Models for Modelling and Pricing Electricity Derivatives," Applied Mathematical Finance, Taylor & Francis Journals, vol. 15(1), pages 41-71.
    20. Nowotarski, Jakub & Tomczyk, Jakub & Weron, Rafał, 2013. "Robust estimation and forecasting of the long-term seasonal component of electricity spot prices," Energy Economics, Elsevier, vol. 39(C), pages 13-27.

    More about this item

    Keywords

    Electricity market; Structural model; Spikes; Forward prices; Spread options; Hedging;
    All these keywords.

    JEL classification:

    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General
    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General

    Statistics

    Access and download statistics

    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:eee:eneeco:v:40:y:2013:i:c:p:976-988. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eneco .

    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.