IDEAS home Printed from https://ideas.repec.org/a/eee/eneeco/v81y2019icp287-296.html
   My bibliography  Save this article

Pricing German Energiewende products: Intraday cap/floor futures

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0140988319301112
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.eneco.2019.04.005?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. Florian Ziel & Rick Steinert, 2015. "Electricity Price Forecasting using Sale and Purchase Curves: The X-Model," Papers 1509.00372, arXiv.org, revised Aug 2016.
    3. François Benhmad & Jacques Percebois, 2018. "Photovoltaic and wind power feed-in impact on electricity prices: The case of Germany," Post-Print hal-01830537, HAL.
    4. Andreas Wagner, 2014. "Residual Demand Modeling and Application to Electricity Pricing," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2).
    5. 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.
    6. 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, September.
    7. 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.
    8. 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.
    9. Hagen Kleinert & Jan Korbel, 2015. "Option Pricing Beyond Black-Scholes Based on Double-Fractional Diffusion," Papers 1503.05655, arXiv.org, revised Mar 2016.
    10. 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.
    11. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    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. Hinderks, W.J. & Wagner, A., 2020. "Factor models in the German electricity market: Stylized facts, seasonality, and calibration," Energy Economics, Elsevier, vol. 85(C).
    2. Hain, Martin & Kargus, Tobias & Schermeyer, Hans & Uhrig-Homburg, Marliese & Fichtner, Wolf, 2022. "An electricity price modeling framework for renewable-dominant markets," Working Paper Series in Production and Energy 66, Karlsruhe Institute of Technology (KIT), Institute for Industrial Production (IIP).
    3. Hain, Martin & Schermeyer, Hans & Uhrig-Homburg, Marliese & Fichtner, Wolf, 2017. "An Electricity Price Modeling Framework for Renewable-Dominant Markets," Working Paper Series in Production and Energy 23, Karlsruhe Institute of Technology (KIT), Institute for Industrial Production (IIP).
    4. Huisman, Ronald & Stet, Cristian, 2022. "The dependence of quantile power prices on supply from renewables," Energy Economics, Elsevier, vol. 105(C).
    5. Shi Chen & Wolfgang Karl Hardle & Brenda L'opez Cabrera, 2020. "Regularization Approach for Network Modeling of German Power Derivative Market," Papers 2009.09739, arXiv.org.
    6. Samarth Kumar & David Schönheit & Matthew Schmidt & Dominik Möst, 2019. "Parsing the Effects of Wind and Solar Generation on the German Electricity Trade Surplus," Energies, MDPI, vol. 12(18), pages 1-17, September.
    7. Maciejowska, Katarzyna, 2020. "Assessing the impact of renewable energy sources on the electricity price level and variability – A quantile regression approach," Energy Economics, Elsevier, vol. 85(C).
    8. Wagner, Andreas & Ramentol, Enislay & Schirra, Florian & Michaeli, Hendrik, 2022. "Short- and long-term forecasting of electricity prices using embedding of calendar information in neural networks," Journal of Commodity Markets, Elsevier, vol. 28(C).
    9. Hain, Martin & Schermeyer, Hans & Uhrig-Homburg, Marliese & Fichtner, Wolf, 2018. "Managing renewable energy production risk," Journal of Banking & Finance, Elsevier, vol. 97(C), pages 1-19.
    10. Ioannidis, Filippos & Kosmidou, Kyriaki & Savva, Christos & Theodossiou, Panayiotis, 2021. "Electricity pricing using a periodic GARCH model with conditional skewness and kurtosis components," Energy Economics, Elsevier, vol. 95(C).
    11. Angelica Gianfreda & Francesco Ravazzolo & Luca Rossini, 2023. "Large Time‐Varying Volatility Models for Hourly Electricity Prices," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(3), pages 545-573, June.
    12. Billé, Anna Gloria & Gianfreda, Angelica & Del Grosso, Filippo & Ravazzolo, Francesco, 2023. "Forecasting electricity prices with expert, linear, and nonlinear models," International Journal of Forecasting, Elsevier, vol. 39(2), pages 570-586.
    13. Peña, Juan Ignacio & Rodríguez, Rosa & Mayoral, Silvia, 2020. "Tail risk of electricity futures," Energy Economics, Elsevier, vol. 91(C).
    14. Rinne, Sonja, 2024. "Estimating the merit-order effect using coarsened exact matching: Reconciling theory with the empirical results to improve policy implications," Energy Policy, Elsevier, vol. 185(C).
    15. Macedo, Daniela Pereira & Marques, António Cardoso & Damette, Olivier, 2022. "The role of electricity flows and renewable electricity production in the behaviour of electricity prices in Spain," Economic Analysis and Policy, Elsevier, vol. 76(C), pages 885-900.
    16. Meus, Jelle & De Vits, Sarah & S'heeren, Nele & Delarue, Erik & Proost, Stef, 2021. "Renewable electricity support in perfect markets: Economic incentives under diverse subsidy instruments," Energy Economics, Elsevier, vol. 94(C).
    17. Gianfreda, Angelica & Ravazzolo, Francesco & Rossini, Luca, 2020. "Comparing the forecasting performances of linear models for electricity prices with high RES penetration," International Journal of Forecasting, Elsevier, vol. 36(3), pages 974-986.
    18. Rafal Weron & Florian Ziel, 2018. "Electricity price forecasting," HSC Research Reports HSC/18/08, Hugo Steinhaus Center, Wroclaw University of Technology.
    19. David Wozabal & Christoph Graf & David Hirschmann, 2016. "The effect of intermittent renewables on the electricity price variance," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 38(3), pages 687-709, July.
    20. Pradhan, Ashis Kumar & Rout, Sandhyarani & Khan, Imran Ahmed, 2021. "Does market concentration affect wholesale electricity prices? An analysis of the Indian electricity sector in the COVID-19 pandemic context," Utilities Policy, Elsevier, vol. 73(C).

    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

    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:eee:eneeco:v:81:y:2019:i:c:p:287-296. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eneco .

    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.