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Pricing German Energiewende products: Intraday cap/floor futures

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  • Hinderks, W.J.
  • Wagner, A.

Abstract

In this paper, we introduce a model for the pricing of German intraday cap/floor futures, introduced by the EEX in 2015. We give a thorough overview of the German intraday market and in particular introduce the ID3 price index, which is the underlying for intraday cap/floor futures. To price these derivatives, we propose a Hull-White model from interest rate theory with seasonality from futures prices. We apply our theoretical results to market data and conduct an empirical analysis involving the initial fit and empirical distribution of intraday cap futures prices.

Suggested Citation

  • Hinderks, W.J. & Wagner, A., 2019. "Pricing German Energiewende products: Intraday cap/floor futures," Energy Economics, Elsevier, vol. 81(C), pages 287-296.
    • Handle: RePEc:eee:eneeco:v:81:y:2019:i:c:p:287-296
    DOI: 10.1016/j.eneco.2019.04.005
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    1. Lars Ivar Hagfors & Hilde Hørthe Kamperud & Florentina Paraschiv & Marcel Prokopczuk & Alma Sator & Sjur Westgaard, 2016. "Prediction of extreme price occurrences in the German day-ahead electricity market," Quantitative Finance, Taylor & Francis Journals, vol. 16(12), pages 1929-1948, December.
    2. Ketterer, Janina C., 2014. "The impact of wind power generation on the electricity price in Germany," Energy Economics, Elsevier, vol. 44(C), pages 270-280.
    3. 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.
    4. Benhmad, François & Percebois, Jacques, 2018. "Photovoltaic and wind power feed-in impact on electricity prices: The case of Germany," Energy Policy, Elsevier, vol. 119(C), pages 317-326.
    5. Erik Gawel & Klaas Korte & Kerstin Tews, 2015. "Distributional Challenges of Sustainability Policies—The Case of the German Energy Transition," Sustainability, MDPI, vol. 7(12), pages 1-17, December.
    6. Caldana, Ruggero & Fusai, Gianluca & Roncoroni, Andrea, 2017. "Electricity forward curves with thin granularity: Theory and empirical evidence in the hourly EPEXspot market," European Journal of Operational Research, Elsevier, vol. 261(2), pages 715-734.
    7. Ziel, Florian & Steinert, Rick, 2016. "Electricity price forecasting using sale and purchase curves: The X-Model," Energy Economics, Elsevier, vol. 59(C), pages 435-454.
    8. Florian Ziel & Rick Steinert, 2015. "Electricity Price Forecasting using Sale and Purchase Curves: The X-Model," Papers 1509.00372, arXiv.org, revised Aug 2016.
    9. François Benhmad & Jacques Percebois, 2018. "Photovoltaic and wind power feed-in impact on electricity prices: The case of Germany," Post-Print hal-01830537, HAL.
    10. Andreas Wagner, 2014. "Residual Demand Modeling and Application to Electricity Pricing," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2).
    11. Hagen Kleinert & Jan Korbel, 2015. "Option Pricing Beyond Black-Scholes Based on Double-Fractional Diffusion," Papers 1503.05655, arXiv.org, revised Mar 2016.
    12. Kleinert, H. & Korbel, J., 2016. "Option pricing beyond Black–Scholes based on double-fractional diffusion," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 449(C), pages 200-214.
    13. Angelica Gianfreda & Derek Bunn, 2018. "A Stochastic Latent Moment Model for Electricity Price Formation," BEMPS - Bozen Economics & Management Paper Series BEMPS46, Faculty of Economics and Management at the Free University of Bozen.
    14. Paraschiv, Florentina & Erni, David & Pietsch, Ralf, 2014. "The impact of renewable energies on EEX day-ahead electricity prices," Energy Policy, Elsevier, vol. 73(C), pages 196-210.
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    Cited by:

    1. Pereira, Diogo Santos & Marques, António Cardoso, 2020. "How should price-responsive electricity tariffs evolve? An analysis of the German net demand case," Utilities Policy, Elsevier, vol. 66(C).
    2. Fred Espen Benth, 2021. "Pricing of Commodity and Energy Derivatives for Polynomial Processes," Mathematics, MDPI, vol. 9(2), pages 1-30, January.
    3. Yuji Yamada & Takuji Matsumoto, 2021. "Going for Derivatives or Forwards? Minimizing Cashflow Fluctuations of Electricity Transactions on Power Markets," Energies, MDPI, vol. 14(21), pages 1-28, November.

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    More about this item

    Keywords

    Intraday cap/floor futures; ID3 price index; German intraday market; Energiewende products; Hull-White model; Factor model;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General

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