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Calibration Of Multifactor Models In Electricity Markets

Author

Listed:
  • MARTIN BARLOW

    (Department of Mathematics, University of British Columbia, Vancouver, Canada V6T 1Z2, Canada)

  • YURI GUSEV

    (Pacific Institute for Mathematical Sciences, University of British Columbia, Vancouver, Canada V6T 1Z2, Canada)

  • MANPO LAI

    (Algorithmics Inc., Toronto, Canada)

Abstract

Spot prices of electricity and other energy commodities are often modeled by multifactor stochastic processes. This poses a problem of estimating models' parameters based on historical data, i.e. calibrating them to markets. Here we show how a traditional tool of Kalman Filters can be successfuly applied to do this task. We study two mean-reverting log-spot price models and the Pilipovic model using correspondingly Kalman Filter the extended Kalman Filter. The results of applying this method to market data from several power exchanges are discussed.

Suggested Citation

  • Martin Barlow & Yuri Gusev & Manpo Lai, 2004. "Calibration Of Multifactor Models In Electricity Markets," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 7(02), pages 101-120.
    • Handle: RePEc:wsi:ijtafx:v:07:y:2004:i:02:n:s0219024904002396
    DOI: 10.1142/S0219024904002396
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    Citations

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    Cited by:

    1. Díaz-Borrego, Francisco J. & Escobar-Peréz, Bernabé & Miras-Rodríguez, María del Mar, 2021. "Estimating copper concentrates benchmark prices under dynamic market conditions," Resources Policy, Elsevier, vol. 70(C).
    2. 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).
    3. Díaz, Guzmán & Moreno, Blanca & Coto, José & Gómez-Aleixandre, Javier, 2015. "Valuation of wind power distributed generation by using Longstaff–Schwartz option pricing method," Applied Energy, Elsevier, vol. 145(C), pages 223-233.
    4. Hinderks, W.J. & Wagner, A., 2020. "Factor models in the German electricity market: Stylized facts, seasonality, and calibration," Energy Economics, Elsevier, vol. 85(C).
    5. Emanuele Fabbiani & Andrea Marziali & Giuseppe De Nicolao, 2018. "Fast calibration of two-factor models for energy option pricing," Papers 1809.03941, arXiv.org, revised Dec 2020.
    6. 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.
    7. Thomas Bollinger & Axel Kind, 2015. "Risk Premiums in the Cross-Section of Commodity Convenience Yields," Working Paper Series of the Department of Economics, University of Konstanz 2015-17, Department of Economics, University of Konstanz.
    8. 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, January.
    9. Maren Diane Schmeck, 2016. "Pricing options on forwards in energy markets: the role of mean reversion's speed," Papers 1602.03402, arXiv.org.

    More about this item

    Keywords

    Electricity markets; mean-reverting commodity prices; multifactor models; stochastic differential equations; Kalman Filters; calibration of market models; JEL classification code: C32; C52; Q40;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General

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