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Electricity price modeling with stochastic time change

Author

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  • Borovkova, Svetlana
  • Schmeck, Maren Diane

Abstract

In this paper, we develop a novel approach to electricity price modeling, based on the powerful technique of stochastic time change. This technique allows us to incorporate the characteristic features of electricity prices (such as seasonal volatility, time varying mean reversion and seasonally occurring price spikes) into the model in an elegant and economically justifiable way. The stochastic time change introduces stochastic as well as deterministic (e.g., seasonal) features in the price process' volatility and in the jump component.

Suggested Citation

  • Borovkova, Svetlana & Schmeck, Maren Diane, 2017. "Electricity price modeling with stochastic time change," Energy Economics, Elsevier, vol. 63(C), pages 51-65.
    • Handle: RePEc:eee:eneeco:v:63:y:2017:i:c:p:51-65
    DOI: 10.1016/j.eneco.2017.01.002
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    1. Jobling, Andrew & Jamasb, Tooraj, 2017. "Price volatility and demand for oil: A comparative analysis of developed and developing countries," Economic Analysis and Policy, Elsevier, vol. 53(C), pages 96-113.
    2. Bessec, Marie & Fouquau, Julien, 2008. "The non-linear link between electricity consumption and temperature in Europe: A threshold panel approach," Energy Economics, Elsevier, vol. 30(5), pages 2705-2721, September.
    3. Helyette Geman & A. Roncoroni, 2006. "Understanding the Fine Structure of Electricity Prices," Post-Print halshs-00144198, HAL.
    4. Härdle, Wolfgang Karl & Burnecki, Krzysztof & Weron, Rafał, 2004. "Simulation of risk processes," Papers 2004,01, Humboldt University of Berlin, Center for Applied Statistics and Economics (CASE).
    5. 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.
    6. 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.
    7. repec:dau:papers:123456789/8180 is not listed on IDEAS
    8. Peter Carr & Hélyette Geman & Dilip B. Madan & Marc Yor, 2003. "Stochastic Volatility for Lévy Processes," Mathematical Finance, Wiley Blackwell, vol. 13(3), pages 345-382, July.
    9. Maren Diane Schmeck, 2016. "Pricing options on forwards in energy markets: the role of mean reversion's speed," Papers 1602.03402, arXiv.org.
    10. Li, Ying & Flynn, Peter C., 2004. "Deregulated power prices: comparison of diurnal patterns," Energy Policy, Elsevier, vol. 32(5), pages 657-672, March.
    11. Yang, C. W. & Hwang, M. J. & Huang, B. N., 2002. "An analysis of factors affecting price volatility of the US oil market," Energy Economics, Elsevier, vol. 24(2), pages 107-119, March.
    12. Clark, Peter K, 1973. "A Subordinated Stochastic Process Model with Finite Variance for Speculative Prices," Econometrica, Econometric Society, vol. 41(1), pages 135-155, January.
    13. Huisman, Ronald & Mahieu, Ronald, 2003. "Regime jumps in electricity prices," Energy Economics, Elsevier, vol. 25(5), pages 425-434, September.
    14. Maren Diane Schmeck, 2016. "Pricing Options On Forwards In Energy Markets: The Role Of Mean Reversion'S Speed," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 19(08), pages 1-26, December.
    15. 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.
    16. Kanamura, Takashi, 2009. "A supply and demand based volatility model for energy prices," Energy Economics, Elsevier, vol. 31(5), pages 736-747, September.
    17. 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.
    18. Ball, Clifford A. & Torous, Walter N., 1983. "A Simplified Jump Process for Common Stock Returns," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 18(1), pages 53-65, March.
    19. Li, Ying & Flynn, Peter C., 2004. "Deregulated power prices: comparison of volatility," Energy Policy, Elsevier, vol. 32(14), pages 1591-1601, September.
    20. Ole E. Barndorff-Nielsen & Fred Espen Benth & Almut E. D. Veraart, 2013. "Modelling energy spot prices by volatility modulated L\'{e}vy-driven Volterra processes," Papers 1307.6332, arXiv.org.
    21. repec:dau:papers:123456789/1433 is not listed on IDEAS
    22. 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.
    23. Paraschiv, Florentina & Fleten, Stein-Erik & Schürle, Michael, 2015. "A spot-forward model for electricity prices with regime shifts," Energy Economics, Elsevier, vol. 47(C), pages 142-153.
    24. Matthew Lorig, 2011. "Time-Changed Fast Mean-Reverting Stochastic Volatility Models," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 14(08), pages 1355-1383.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Electricity prices; Stochastic time change; Activity rate; Mean reversion; Jump diffusion;
    All these keywords.

    JEL classification:

    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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