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Modeling European Electricity Market Integration during turbulent times

Author

Listed:
  • Francesco Ravazzolo

    (Norwegian Business School, Norway; Free-University of Bozen-Bolzano, Italy; Rimini Centre for Economic Analysis)

  • Luca Rossini

    (University of Milan, Italy; Fondazione Eni Enrico Mattei, Italy)

  • Andrea Viselli

    (University of Milan, Italy)

Abstract

This paper introduces a novel Bayesian reverse unrestricted mixed-frequency model applied to a panel of nine European electricity markets. Our model analyzes the impact of daily fossil fuel prices and hourly renewable energy generation on hourly electricity prices, employing a hierarchical structure to capture cross-country interdependencies and idiosyncratic factors. The inclusion of random effects demonstrates that electricity market integration both mitigates and amplifies shocks. Our results highlight that while renewable energy sources consistently reduce electricity prices across all countries, gas prices remain a dominant driver of cross-country electricity price disparities and instability. This finding underscores the critical importance of energy diversification, above all on renewable energy sources, and coordinated fossil fuel supply strategies for bolstering European energy security.

Suggested Citation

  • Francesco Ravazzolo & Luca Rossini & Andrea Viselli, 2025. "Modeling European Electricity Market Integration during turbulent times," Working Paper series 25-06, Rimini Centre for Economic Analysis.
    • Handle: RePEc:rim:rimwps:25-06
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    References listed on IDEAS

    as
    1. 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.
    2. Fabio Canova & Matteo Ciccarelli, 2009. "Estimating Multicountry Var Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 50(3), pages 929-959, August.
    3. Foroni, Claudia & Guérin, Pierre & Marcellino, Massimiliano, 2018. "Using low frequency information for predicting high frequency variables," International Journal of Forecasting, Elsevier, vol. 34(4), pages 774-787.
    4. 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.
    5. 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.
    6. Chib, Siddhartha & Greenberg, Edward, 1995. "Hierarchical analysis of SUR models with extensions to correlated serial errors and time-varying parameter models," Journal of Econometrics, Elsevier, vol. 68(2), pages 339-360, August.
    7. Eric Ghysels & Pedro Santa-Clara & Rossen Valkanov, 2004. "The MIDAS Touch: Mixed Data Sampling Regression Models," CIRANO Working Papers 2004s-20, CIRANO.
    8. Foroni, Claudia & Ravazzolo, Francesco & Rossini, Luca, 2023. "Are low frequency macroeconomic variables important for high frequency electricity prices?," Economic Modelling, Elsevier, vol. 120(C).
    9. Hidalgo-Pérez, Manuel & Collado, Natalia & Galindo, Jorge & Mateo, Ramón, 2024. "The Iberian exception: Estimating the impact of a cap on gas prices for electricity generation on consumer prices and market dynamics," Energy Policy, Elsevier, vol. 188(C).
    10. Claudia Foroni & Massimiliano Marcellino & Christian Schumacher, 2015. "Unrestricted mixed data sampling (MIDAS): MIDAS regressions with unrestricted lag polynomials," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(1), pages 57-82, January.
    11. repec:cdl:anderf:qt9mf223rs is not listed on IDEAS
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    Keywords

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    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • 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
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General

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