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Modeling positive electricity prices with arithmetic jump-diffusions

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  • Hess, Markus

Abstract

We propose a mean-reverting electricity spot price model of arithmetic jump-diffusion type yielding positive prices. Based on this approach, we derive the corresponding forward and futures price representations. We further discuss different choices for the stochastic mean level process and investigate the long-term behavior of the spot price. In the second part, we take future information available to the traders into account. The latter is modeled by initially enlarged filtrations with respect to (a) the mean level of the spot, (b) the driving diffusion component and (c) the jump term. We also derive forward and futures price representations under these enlarged filtrations. Finally, we consider the evaluation of options in the proposed models.

Suggested Citation

  • Hess, Markus, 2017. "Modeling positive electricity prices with arithmetic jump-diffusions," Energy Economics, Elsevier, vol. 67(C), pages 496-507.
  • Handle: RePEc:eee:eneeco:v:67:y:2017:i:c:p:496-507
    DOI: 10.1016/j.eneco.2017.08.016
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    References listed on IDEAS

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    1. Claudia Kluppelberg & Thilo Meyer-Brandis & Andrea Schmidt, 2010. "Electricity spot price modelling with a view towards extreme spike risk," Quantitative Finance, Taylor & Francis Journals, vol. 10(9), pages 963-974.
    2. Thilo Meyer-Brandis & Peter Tankov, 2008. "Multi-Factor Jump-Diffusion Models Of Electricity Prices," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 11(05), pages 503-528.
    3. Helyette Geman & A. Roncoroni, 2006. "Understanding the Fine Structure of Electricity Prices," Post-Print halshs-00144198, HAL.
    4. Markus Hess, 2016. "Modeling and pricing precipitation derivatives under weather forecasts," ULB Institutional Repository 2013/247729, ULB -- Universite Libre de Bruxelles.
    5. Benth, Fred Espen & Klüppelberg, Claudia & Müller, Gernot & Vos, Linda, 2014. "Futures pricing in electricity markets based on stable CARMA spot models," Energy Economics, Elsevier, vol. 44(C), pages 392-406.
    6. M. T. Barlow, 2002. "A Diffusion Model For Electricity Prices," Mathematical Finance, Wiley Blackwell, vol. 12(4), pages 287-298, October.
    7. Markus Hess, 2016. "Modeling And Pricing Precipitation Derivatives Under Weather Forecasts," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 19(07), pages 1-29, November.
    8. 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.
    9. Zhao, Juan, 2009. "Long time behaviour of stochastic interest rate models," Insurance: Mathematics and Economics, Elsevier, vol. 44(3), pages 459-463, June.
    10. Fu, Zongfei & Li, Zenghu, 2010. "Stochastic equations of non-negative processes with jumps," Stochastic Processes and their Applications, Elsevier, vol. 120(3), pages 306-330, March.
    11. Juri Hinz & Lutz Von Grafenstein & Michel Verschuere & Martina Wilhelm, 2005. "Pricing electricity risk by interest rate methods," Quantitative Finance, Taylor & Francis Journals, vol. 5(1), pages 49-60.
    12. repec:dau:papers:123456789/1433 is not listed on IDEAS
    13. 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.
    14. Almut E. D. Veraart & Luitgard A. M. Veraart, 2013. "Risk premia in energy markets," CREATES Research Papers 2013-02, Department of Economics and Business Economics, Aarhus University.
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    Cited by:

    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. 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.
    3. Liu, Xiaoyang & He, Daobing & Liu, Chao, 2018. "Modeling information dissemination and evolution in time-varying online social network based on thermal diffusion motion," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 456-476.

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    More about this item

    Keywords

    Stochastic calculus; Positivity of solution to stochastic differential equation; Ornstein-Uhlenbeck process; Enlargement of filtration; Future information; Insider trading; Arithmetic jump-diffusion model; Long-term behavior; Electricity spot/forward/futures price; Option pricing;
    All these keywords.

    JEL classification:

    • D52 - Microeconomics - - General Equilibrium and Disequilibrium - - - Incomplete Markets
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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