IDEAS home Printed from https://ideas.repec.org/p/dui/wpaper/1804.html
   My bibliography  Save this paper

Estimating the value of flexibility from real options: On the accuracy of hybrid electricity price models

Author

Listed:
  • Christian Pape
  • Oliver Woll
  • Christoph Weber

    (Chair for Management Sciences and Energy Economics, University of Duisburg-Essen (Campus Essen))

Abstract

Practitioners in the electricity industry aim to assess the value of power plants or other real options several months or even years ahead of operation. Such a valuation is notably required for hedging purposes. The revenue streams to be earned in the spot market are thereby already secured on future markets. Yet the peculiarities of the electricity market, notably the limited storability of electricity and the incompleteness of the derivative markets, make this problem also theoretically challenging since they prevent the straightforward application of standard approaches for price modeling and for hedging. In this context, the contribution of this article is twofold: (1) We present a novel methodology to model electricity prices based on fundamental expectations and accounting for both short-term and long-term uncertainties. This requires the joint modeling of different commodity prices, namely electricity, fuel and CO2 prices. Moreover price distributions have to be modelled in order to assess the real option value adequately ex ante. Specifically, we compare two different modeling approaches to account for long-term variations in multi-commodity price dynamics. (2) We suggest a test procedure and introduce performance measures to analyze the accuracy of the proposed price modeling. We thereby focus on the practically relevant question, whether the price modeling provides ex ante estimates of the value of the real option that are in line with the ex post realized values. This approach is chosen since no derivative markets exist where the (extrinsic) values for the real options could be observed months or years ahead of actual operation. Nonetheless we show that under well-defined assumptions, the ex-ante values derived using the price model should provide unbiased estimates of the ex post values, which are computed as a sum of hedging and spot exercise revenues. The application part shows results for a state-of-the-art gas power plant. By applying the developed performance measures and test statistics, we find that neither of the two investigated price models clearly outperforms the other.

Suggested Citation

  • Christian Pape & Oliver Woll & Christoph Weber, "undated". "Estimating the value of flexibility from real options: On the accuracy of hybrid electricity price models," EWL Working Papers 1804, University of Duisburg-Essen, Chair for Management Science and Energy Economics.
  • Handle: RePEc:dui:wpaper:1804
    as

    Download full text from publisher

    File URL: https://www.wiwi.uni-due.de/fileadmin/fileupload/BWL-ENERGIE/Arbeitspapiere/RePEc/pdf/wp1804_EstimatingTheValueOfFlexibilityFromRealOptionsOnTheAccuracyOfHybridElectricityPriceModels.PDF
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kallabis, Thomas & Pape, Christian & Weber, Christoph, 2016. "The plunge in German electricity futures prices – Analysis using a parsimonious fundamental model," Energy Policy, Elsevier, vol. 95(C), pages 280-290.
    2. Joëts, Marc & Mignon, Valérie, 2012. "On the link between forward energy prices: A nonlinear panel cointegration approach," Energy Economics, Elsevier, vol. 34(4), pages 1170-1175.
    3. Huisman, Ronald & Huurman, Christian & Mahieu, Ronald, 2007. "Hourly electricity prices in day-ahead markets," Energy Economics, Elsevier, vol. 29(2), pages 240-248, March.
    4. Gondzio, Jacek & Kouwenberg, Roy & Vorst, Ton, 2003. "Hedging options under transaction costs and stochastic volatility," Journal of Economic Dynamics and Control, Elsevier, vol. 27(6), pages 1045-1068, April.
    5. Raimund Kovacevic & David Wozabal, 2014. "A semiparametric model for electricity spot prices," IISE Transactions, Taylor & Francis Journals, vol. 46(4), pages 344-356.
    6. Panagiotidis, Theodore & Rutledge, Emilie, 2007. "Oil and gas markets in the UK: Evidence from a cointegrating approach," Energy Economics, Elsevier, vol. 29(2), pages 329-347, March.
    7. Maciejowska, Katarzyna & Nowotarski, Jakub, 2016. "A hybrid model for GEFCom2014 probabilistic electricity price forecasting," International Journal of Forecasting, Elsevier, vol. 32(3), pages 1051-1056.
    8. Gibson, Rajna & Schwartz, Eduardo S, 1990. "Stochastic Convenience Yield and the Pricing of Oil Contingent Claims," Journal of Finance, American Finance Association, vol. 45(3), pages 959-976, July.
    9. Mjelde, James W. & Bessler, David A., 2009. "Market integration among electricity markets and their major fuel source markets," Energy Economics, Elsevier, vol. 31(3), pages 482-491, May.
    10. Tilmann Gneiting & Fadoua Balabdaoui & Adrian E. Raftery, 2007. "Probabilistic forecasts, calibration and sharpness," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(2), pages 243-268, April.
    11. Huisman, Ronald & Mahieu, Ronald, 2003. "Regime jumps in electricity prices," Energy Economics, Elsevier, vol. 25(5), pages 425-434, September.
    12. Pape, Christian & Hagemann, Simon & Weber, Christoph, 2016. "Are fundamentals enough? Explaining price variations in the German day-ahead and intraday power market," Energy Economics, Elsevier, vol. 54(C), pages 376-387.
    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. Christian Pape & Arne Vogler & Oliver Woll & Christoph Weber, 2017. "Forecasting the distributions of hourly electricity spot prices," EWL Working Papers 1705, University of Duisburg-Essen, Chair for Management Science and Energy Economics, revised May 2017.
    2. 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.
    3. 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).
    4. Dias, José G. & Ramos, Sofia B., 2014. "Energy price dynamics in the U.S. market. Insights from a heterogeneous multi-regime framework," Energy, Elsevier, vol. 68(C), pages 327-336.
    5. Moutinho, Victor & Vieira, Joel & Carrizo Moreira, António, 2011. "The crucial relationship among energy commodity prices: Evidence from the Spanish electricity market," Energy Policy, Elsevier, vol. 39(10), pages 5898-5908, October.
    6. Mira Watermeyer & Thomas Mobius & Oliver Grothe & Felix Musgens, 2023. "A hybrid model for day-ahead electricity price forecasting: Combining fundamental and stochastic modelling," Papers 2304.09336, arXiv.org.
    7. Nowotarski, Jakub & Weron, Rafał, 2018. "Recent advances in electricity price forecasting: A review of probabilistic forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1548-1568.
    8. Radu Porumb & Petru Postolache & George Serițan & Ramona Vatu & Oana Ceaki, 2013. "Load profiles analysis for electricity market," Computational Methods in Social Sciences (CMSS), "Nicolae Titulescu" University of Bucharest, Faculty of Economic Sciences, vol. 1(2), pages 30-38, December.
    9. Mason, Charles F. & A. Wilmot, Neil, 2014. "Jump processes in natural gas markets," Energy Economics, Elsevier, vol. 46(S1), pages 69-79.
    10. Egil Ferkingstad & Anders L{o}land & Mathilde Wilhelmsen, 2011. "Causal modeling and inference for electricity markets," Papers 1110.5429, arXiv.org.
    11. Christopher Kath & Florian Ziel, 2018. "The value of forecasts: Quantifying the economic gains of accurate quarter-hourly electricity price forecasts," Papers 1811.08604, arXiv.org.
    12. Andreas Dietrich, 2023. "Incentives for flexible consumption and production on end-user level - Evidence from a German case study and outlook for 2030 -," EWL Working Papers 2302, University of Duisburg-Essen, Chair for Management Science and Energy Economics, revised Feb 2023.
    13. Andreas Gerster, 2016. "Negative price spikes at power markets: the role of energy policy," Journal of Regulatory Economics, Springer, vol. 50(3), pages 271-289, December.
    14. Escoffier, Margaux & Hache, Emmanuel & Mignon, Valérie & Paris, Anthony, 2021. "Determinants of solar photovoltaic deployment in the electricity mix: Do oil prices really matter?," Energy Economics, Elsevier, vol. 97(C).
    15. Carlo Fezzi & Luca Mosetti, 2018. "Size matters: Estimation sample length and electricity price forecasting accuracy," DEM Working Papers 2018/10, Department of Economics and Management.
    16. Avci, Ezgi & Ketter, Wolfgang & van Heck, Eric, 2018. "Managing electricity price modeling risk via ensemble forecasting: The case of Turkey," Energy Policy, Elsevier, vol. 123(C), pages 390-403.
    17. Joëts, Marc & Mignon, Valérie, 2012. "On the link between forward energy prices: A nonlinear panel cointegration approach," Energy Economics, Elsevier, vol. 34(4), pages 1170-1175.
    18. 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.
    19. Huisman, Ronald & Huurman, Christian & Mahieu, Ronald, 2007. "Hourly electricity prices in day-ahead markets," Energy Economics, Elsevier, vol. 29(2), pages 240-248, March.
    20. repec:ipg:wpaper:2013-028 is not listed on IDEAS
    21. Kath, Christopher & Ziel, Florian, 2018. "The value of forecasts: Quantifying the economic gains of accurate quarter-hourly electricity price forecasts," Energy Economics, Elsevier, vol. 76(C), pages 411-423.

    More about this item

    Keywords

    Electricity price forecasting; Futures market; Hedging; Real option Stochastic optimization∙ Valuation;
    All these keywords.

    JEL classification:

    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q48 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Government Policy

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:dui:wpaper:1804. 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: Andreas Fritz (email available below). General contact details of provider: https://edirc.repec.org/data/fwessde.html .

    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.