IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i17p4764-d1744380.html
   My bibliography  Save this article

Speculators and Price Inertia in a Day-Ahead Electricity Market: An Irish Case Study

Author

Listed:
  • Joseph Collins

    (School of Mathematical Sciences, University College Cork, T12 YN60 Cork, Ireland)

  • Andreas Amann

    (School of Mathematical Sciences, University College Cork, T12 YN60 Cork, Ireland
    These authors contributed equally to this work.)

  • Kieran Mulchrone

    (School of Mathematical Sciences, University College Cork, T12 YN60 Cork, Ireland
    These authors contributed equally to this work.)

Abstract

Short-term dynamics in auction-based Day-Ahead electricity markets remain insufficiently studied. This paper investigates two such aspects in the Irish Day-Ahead market. First, we address an empirical gap by examining the extent of speculator (financial trader) participation and its evolution over time in a European Day-Ahead setting. Using granular participant-level order and trade data, we quantify speculators’ share of overall market activity and assess how often they are marginal in the auction. Although their share of orders and trades is relatively small, speculators are marginal in a substantial proportion of trading periods and their behaviour changes significantly following a Brexit-related structural shift in market coupling. Second, we introduce a sensitivity-based measure of price inertia defined as the resistance of prices to small changes in demand or supply, adapted to the Day-Ahead auction context, a dimension of market behaviour that has received little prior attention. We find that inertia levels vary considerably and also shift following the structural change. Taken together, these analyses provide empirical evidence that speculators play a non-negligible role in a European auction-based Day-Ahead market, while price inertia offers an additional lens through which to examine short-term market dynamics and their evolution under different market conditions.

Suggested Citation

  • Joseph Collins & Andreas Amann & Kieran Mulchrone, 2025. "Speculators and Price Inertia in a Day-Ahead Electricity Market: An Irish Case Study," Energies, MDPI, vol. 18(17), pages 1-26, September.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:17:p:4764-:d:1744380
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/17/4764/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/18/17/4764/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Beran, Philip & Pape, Christian & Weber, Christoph, 2019. "Modelling German electricity wholesale spot prices with a parsimonious fundamental model – Validation & application," Utilities Policy, Elsevier, vol. 58(C), pages 27-39.
    3. Florian Ziel & Rick Steinert, 2015. "Electricity Price Forecasting using Sale and Purchase Curves: The X-Model," Papers 1509.00372, arXiv.org, revised Aug 2016.
    4. Hellström, Jörgen & Lundgren, Jens & Yu, Haishan, 2012. "Why do electricity prices jump? Empirical evidence from the Nordic electricity market," Energy Economics, Elsevier, vol. 34(6), pages 1774-1781.
    5. Frank Wolak, 2000. "An Empirical Analysis of the Impact of Hedge Contracts on Bidding Behavior in a Competitive Electricity Market," International Economic Journal, Taylor & Francis Journals, vol. 14(2), pages 1-39.
    6. Mariusz Kaleta, 2025. "Robust Co-Optimization of Medium- and Short-Term Electrical Energy and Flexibility in Electricity Clusters," Energies, MDPI, vol. 18(3), pages 1-23, January.
    7. Umut Ugurlu & Ilkay Oksuz & Oktay Tas, 2018. "Electricity Price Forecasting Using Recurrent Neural Networks," Energies, MDPI, vol. 11(5), pages 1-23, May.
    8. Birge, John R. & Hortaçsu, Ali & Mercadal, Ignacia & Pavlin, J. Michael, 2018. "Limits to arbitrage in electricity markets: A case study of MISO," Energy Economics, Elsevier, vol. 75(C), pages 518-533.
    9. Ullrich, Carl J., 2012. "Realized volatility and price spikes in electricity markets: The importance of observation frequency," Energy Economics, Elsevier, vol. 34(6), pages 1809-1818.
    10. Koichiro Ito & Mar Reguant, 2016. "Sequential Markets, Market Power, and Arbitrage," American Economic Review, American Economic Association, vol. 106(7), pages 1921-1957, July.
    11. Bask, Mikael & Widerberg, Anna, 2009. "Market structure and the stability and volatility of electricity prices," Energy Economics, Elsevier, vol. 31(2), pages 278-288, March.
    12. Liebl, Dominik, 2013. "Modeling and Forecasting Electricity Spot Prices: A Functional Data Perspective," MPRA Paper 50881, University Library of Munich, Germany.
    13. Aitor Ciarreta & María Espinosa, 2010. "Market power in the Spanish electricity auction," Journal of Regulatory Economics, Springer, vol. 37(1), pages 42-69, February.
    14. Mayer, Klaus & Trück, Stefan, 2018. "Electricity markets around the world," Journal of Commodity Markets, Elsevier, vol. 9(C), pages 77-100.
    15. Fabra, Natalia, 2023. "Reforming European electricity markets: Lessons from the energy crisis," Energy Economics, Elsevier, vol. 126(C).
    16. Li, Kun & Cursio, Joseph D. & Jiang, Mengfei & Liang, Xi, 2019. "The significance of calendar effects in the electricity market," Applied Energy, Elsevier, vol. 235(C), pages 487-494.
    17. 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.
    18. Emanuel Canelas & Tânia Pinto-Varela & Bartosz Sawik, 2020. "Electricity Portfolio Optimization for Large Consumers: Iberian Electricity Market Case Study," Energies, MDPI, vol. 13(9), pages 1-21, May.
    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. Mahler, Valentin & Girard, Robin & Kariniotakis, Georges, 2022. "Data-driven structural modeling of electricity price dynamics," Energy Economics, Elsevier, vol. 107(C).
    2. Sirin, Selahattin Murat & Camadan, Ercument & Erten, Ibrahim Etem & Zhang, Alex Hongliang, 2023. "Market failure or politics? Understanding the motives behind regulatory actions to address surging electricity prices," Energy Policy, Elsevier, vol. 180(C).
    3. Valentin Mahler & Robin Girard & Georges Kariniotakis, 2021. "Data-driven Structural Modeling of Electricity Price Dynamics," Working Papers hal-03445396, HAL.
    4. Ilkay Oksuz & Umut Ugurlu, 2019. "Neural Network Based Model Comparison for Intraday Electricity Price Forecasting," Energies, MDPI, vol. 12(23), pages 1-14, November.
    5. Philip Beran & Arne Vogler, 2021. "Multi-Day-Ahead Electricity Price Forecasting: A Comparison of fundamental, econometric and hybrid Models," EWL Working Papers 2102, University of Duisburg-Essen, Chair for Management Science and Energy Economics, revised Oct 2021.
    6. Micha{l} Narajewski & Florian Ziel, 2021. "Optimal bidding in hourly and quarter-hourly electricity price auctions: trading large volumes of power with market impact and transaction costs," Papers 2104.14204, arXiv.org, revised Feb 2022.
    7. Ethem Çanakoğlu & Esra Adıyeke, 2020. "Comparison of Electricity Spot Price Modelling and Risk Management Applications," Energies, MDPI, vol. 13(18), pages 1-22, September.
    8. Rafal Weron & Florian Ziel, 2018. "Electricity price forecasting," HSC Research Reports HSC/18/08, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
    9. Arcos-Vargas, A. & Nuñez, F. & Román-Collado, R., 2020. "Short-term effects of PV integration on global welfare and CO2 emissions. An application to the Iberian electricity market," Energy, Elsevier, vol. 200(C).
    10. 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).
    11. Ismail Shah & Francesco Lisi, 2020. "Forecasting of electricity price through a functional prediction of sale and purchase curves," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(2), pages 242-259, March.
    12. Rassi, Samin & Kanamura, Takashi, 2023. "Electricity price spike formation and LNG prices effect under gross bidding scheme in JEPX," Energy Policy, Elsevier, vol. 177(C).
    13. Kohút, Roman & Klaučo, Martin & Kvasnica, Michal, 2025. "Unified carbon emissions and market prices forecasts of the power grid," Applied Energy, Elsevier, vol. 377(PC).
    14. Nadja Klein & Michael Stanley Smith & David J. Nott, 2023. "Deep distributional time series models and the probabilistic forecasting of intraday electricity prices," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(4), pages 493-511, June.
    15. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    16. Paul Ghelasi & Florian Ziel, 2024. "From day-ahead to mid and long-term horizons with econometric electricity price forecasting models," Papers 2406.00326, arXiv.org, revised Aug 2024.
    17. Van Moer, Geert, 2019. "Electricity market competition when forward contracts are pairwise efficient," MPRA Paper 96660, University Library of Munich, Germany.
    18. Bohland, Moritz & Schwenen, Sebastian, 2022. "Renewable support and strategic pricing in electricity markets," International Journal of Industrial Organization, Elsevier, vol. 80(C).
    19. Florian Ziel & Rick Steinert, 2017. "Probabilistic Mid- and Long-Term Electricity Price Forecasting," Papers 1703.10806, arXiv.org, revised May 2018.
    20. Keles, Dogan & Dehler-Holland, Joris & Densing, Martin & Panos, Evangelos & Hack, Felix, 2020. "Cross-border effects in interconnected electricity markets - an analysis of the Swiss electricity prices," Energy Economics, Elsevier, vol. 90(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    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:gam:jeners:v:18:y:2025:i:17:p:4764-:d:1744380. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.