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A regime-switching copula approach to modeling day-ahead prices in coupled electricity markets

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  • Pircalabu, A.
  • Benth, F.E.

Abstract

The recent price coupling of many European electricity markets has triggered a fundamental change in the interaction of day-ahead prices, challenging additionally the modeling of the joint behavior of prices in interconnected markets. In this paper we propose a regime-switching AR–GARCH copula to model pairs of day-ahead electricity prices in coupled European markets. While capturing key stylized facts empirically substantiated in the literature, this model easily allows us to 1) deviate from the assumption of normal margins and 2) include a more detailed description of the dependence between prices. We base our empirical study on four pairs of prices, namely Germany–France, Germany–Netherlands, Netherlands–Belgium and Germany–Western Denmark. We find that the marginal dynamics are better described by the flexible skew t distribution than the benchmark normal distribution. Also, we find significant evidence of tail dependence in all pairs of interconnected areas we consider. As a first application of the proposed model, we consider the pricing of financial transmission rights, and highlight how the choice of marginal distributions and copula impacts prices. As a second application we consider the forecasting of tail quantiles, and evaluate the out-of-sample performance of competing models.

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  • Pircalabu, A. & Benth, F.E., 2017. "A regime-switching copula approach to modeling day-ahead prices in coupled electricity markets," Energy Economics, Elsevier, vol. 68(C), pages 283-302.
  • Handle: RePEc:eee:eneeco:v:68:y:2017:i:c:p:283-302
    DOI: 10.1016/j.eneco.2017.10.008
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    Cited by:

    1. Oliver Grothe & Fabian Kachele & Fabian Kruger, 2022. "From point forecasts to multivariate probabilistic forecasts: The Schaake shuffle for day-ahead electricity price forecasting," Papers 2204.10154, arXiv.org.
    2. Kumar, Satish & Tiwari, Aviral Kumar & Raheem, Ibrahim Dolapo & Hille, Erik, 2021. "Time-varying dependence structure between oil and agricultural commodity markets: A dependence-switching CoVaR copula approach," Resources Policy, Elsevier, vol. 72(C).
    3. Díaz, Guzmán & Coto, José & Gómez-Aleixandre, Javier, 2019. "Levelized income loss as a metric of the adaptation of wind and energy storage to variable prices," Applied Energy, Elsevier, vol. 238(C), pages 1179-1191.
    4. Liu, Xiang-dong & Pan, Fei & Cai, Wen-li & Peng, Rui, 2020. "Correlation and risk measurement modeling: A Markov-switching mixed Clayton copula approach," Reliability Engineering and System Safety, Elsevier, vol. 197(C).
    5. Grothe, Oliver & Kächele, Fabian & Krüger, Fabian, 2023. "From point forecasts to multivariate probabilistic forecasts: The Schaake shuffle for day-ahead electricity price forecasting," Energy Economics, Elsevier, vol. 120(C).
    6. Carbajal-De-Nova, Carolina & Venegas-Martínez, Francisco, 2019. "On the paradigm shift of asset pricing models, before and after the global financial crisis: a literature review," Panorama Económico, Escuela Superior de Economía, Instituto Politécnico Nacional, vol. 15(29), pages 7-38, Primer se.
    7. Loi, Tian Sheng Allan & Ng, Jia Le, 2018. "Anticipating electricity prices for future needs – Implications for liberalised retail markets," Applied Energy, Elsevier, vol. 212(C), pages 244-264.
    8. Fabrizio Durante & Angelica Gianfreda & Francesco Ravazzolo & Luca Rossini, 2022. "A Multivariate Dependence Analysis for Electricity Prices, Demand and Renewable Energy Sources," Papers 2201.01132, arXiv.org.
    9. Brandão, Lucas G.L. & Ehrl, Philipp, 2022. "The impact of transmission auctions on Brazilian electric power companies," Utilities Policy, Elsevier, vol. 78(C).
    10. Aviral Kumar Tiwari & Sangram Keshari Jena & Satish Kumar & Erik Hille, 2022. "Is oil price risk systemic to sectoral equity markets of an oil importing country? Evidence from a dependence-switching copula delta CoVaR approach," Annals of Operations Research, Springer, vol. 315(1), pages 429-461, August.
    11. Godin, Frédéric & Ibrahim, Zinatu, 2021. "An analysis of electricity congestion price patterns in North America," Energy Economics, Elsevier, vol. 102(C).
    12. Nadja Klein & Michael Stanley Smith & David J. Nott, 2020. "Deep Distributional Time Series Models and the Probabilistic Forecasting of Intraday Electricity Prices," Papers 2010.01844, arXiv.org, revised May 2021.
    13. Mehrdoust, Farshid & Noorani, Idin & Kanniainen, Juho, 2024. "Valuation of option price in commodity markets described by a Markov-switching model: A case study of WTI crude oil market," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 215(C), pages 228-269.
    14. Kumar, Satish & Tiwari, Aviral Kumar & Chauhan, Yogesh & Ji, Qiang, 2019. "Dependence structure between the BRICS foreign exchange and stock markets using the dependence-switching copula approach," International Review of Financial Analysis, Elsevier, vol. 63(C), pages 273-284.
    15. Simon Hirsch & Florian Ziel, 2023. "Multivariate Simulation-based Forecasting for Intraday Power Markets: Modelling Cross-Product Price Effects," Papers 2306.13419, arXiv.org.

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    More about this item

    Keywords

    Day-ahead electricity prices; Market coupling; Copula models; Tail dependence; Financial transmission rights; Tail quantile forecasting;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy

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