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Distributional forecasting of electricity DART spreads with a covariate-dependent mixture model

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  • Forgetta, Anthony
  • Godin, Frédéric
  • Augustyniak, Maciej

Abstract

We develop a covariate-dependent mixture model to describe the behavior of electricity DART spreads, which are differentials between day-ahead and real-time prices of electricity. The model includes three regimes: a regular DART regime, a positive spike regime, and a negative spike regime. The model exhibits sufficient flexibility to allow covariates impacting both the frequency and severity of DART spread spikes, and to reproduce salient stylized facts of DART spread dynamics. The covariates considered include forecasts for load, weather, and natural gas prices. The application of our model on data from the Long Island zone of the NYISO (New York Independent System Operator) exhibits a satisfactory fit to the data. Numerical experiments reveal that including covariates in the severity component of the model is crucial, while mild additional performance is obtained with their inclusion in the frequency component. Furthermore, neural network-based quantile regression benchmarks are unable to improve performance over our mixture model.

Suggested Citation

  • Forgetta, Anthony & Godin, Frédéric & Augustyniak, Maciej, 2025. "Distributional forecasting of electricity DART spreads with a covariate-dependent mixture model," Energy Economics, Elsevier, vol. 144(C).
    • Handle: RePEc:eee:eneeco:v:144:y:2025:i:c:s0140988325001562
    DOI: 10.1016/j.eneco.2025.108332
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    References listed on IDEAS

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    1. Claudia Kluppelberg & Thilo Meyer-Brandis & Andrea Schmidt, 2010. "Electricity spot price modelling with a view towards extreme spike risk," Quantitative Finance, Taylor & Francis Journals, vol. 10(9), pages 963-974.
    2. Marcjasz, Grzegorz & Narajewski, Michał & Weron, Rafał & Ziel, Florian, 2023. "Distributional neural networks for electricity price forecasting," Energy Economics, Elsevier, vol. 125(C).
    3. Galarneau-Vincent, Rémi & Gauthier, Geneviève & Godin, Frédéric, 2023. "Foreseeing the worst: Forecasting electricity DART spikes," Energy Economics, Elsevier, vol. 119(C).
    4. Fred Espen Benth & Jan Kallsen & Thilo Meyer-Brandis, 2007. "A Non-Gaussian Ornstein-Uhlenbeck Process for Electricity Spot Price Modeling and Derivatives Pricing," Applied Mathematical Finance, Taylor & Francis Journals, vol. 14(2), pages 153-169.
    5. 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.
    6. Rafal Weron, 2014. "A review of electricity price forecasting: The past, the present and the future," HSC Research Reports HSC/14/02, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
    7. Roger Koenker & Kevin F. Hallock, 2001. "Quantile Regression," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 143-156, Fall.
    8. 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).
    9. repec:bla:jfinan:v:59:y:2004:i:4:p:1877-1900 is not listed on IDEAS
    10. 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.
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    Keywords

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    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities
    • N72 - Economic History - - Economic History: Transport, International and Domestic Trade, Energy, and Other Services - - - U.S.; Canada: 1913-

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