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Modeling extreme dependence between European electricity markets

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  • Lindström, Erik
  • Regland, Fredrik

Abstract

Electricity spot prices are characterized by sudden large movements, followed a few days later by an equally large movement in the opposite direction. These phenomena are called spikes (upward movements) and drops (downward movements). Recent research has suggested that the dynamics of the electricity spot prices can be accurately described by hidden Markov Regime Switching (MRS) models. Regime switch models separate the ordinary dependence and the extreme (spike or drop) dependence. This is a crucial point since it is the extreme dependence that is of interest when computing risks.

Suggested Citation

  • Lindström, Erik & Regland, Fredrik, 2012. "Modeling extreme dependence between European electricity markets," Energy Economics, Elsevier, vol. 34(4), pages 899-904.
  • Handle: RePEc:eee:eneeco:v:34:y:2012:i:4:p:899-904
    DOI: 10.1016/j.eneco.2012.04.006
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    References listed on IDEAS

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    Cited by:

    1. Stephen Machin & Olivier Marie & Sunčica Vujić, 2012. "Youth Crime and Education Expansion," German Economic Review, Verein für Socialpolitik, vol. 13(4), pages 366-384, November.
    2. repec:eee:appene:v:211:y:2018:i:c:p:890-903 is not listed on IDEAS
    3. A Ciarreta and A Zarraga, 2015. "Analysis of mean and volatility price transmissions in the MIBEL and EPEX electricity spot markets," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4).
    4. Jesus Lago & Fjo De Ridder & Peter Vrancx & Bart De Schutter, 2017. "Forecasting day-ahead electricity prices in Europe: the importance of considering market integration," Papers 1708.07061, arXiv.org, revised Dec 2017.
    5. Eichler, M. & Türk, D., 2013. "Fitting semiparametric Markov regime-switching models to electricity spot prices," Energy Economics, Elsevier, vol. 36(C), pages 614-624.
    6. Grossi, Luigi & Heim, Sven & Waterson, Michael, 2014. "A vision of the European energy future? The impact of the German response to the Fukushima earthquake," The Warwick Economics Research Paper Series (TWERPS) 1047, University of Warwick, Department of Economics.
    7. Lindström, Erik & Norén, Vicke & Madsen, Henrik, 2015. "Consumption management in the Nord Pool region: A stability analysis," Applied Energy, Elsevier, vol. 146(C), pages 239-246.
    8. Ciarreta Antuñano, Aitor & Zárraga Alonso, Ainhoa, 2012. "Analysis of volatility transmissions in integrated and interconnected markets: The case of the Iberian and French markets," BILTOKI Biltoki;2012-04, Universidad del País Vasco - Departamento de Economía Aplicada III (Econometría y Estadística).
    9. Bigerna, Simona & Bollino, Carlo Andrea & Ciferri, Davide & Polinori, Paolo, 2017. "Renewables diffusion and contagion effect in Italian regional electricity markets: Assessment and policy implications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P1), pages 199-211.
    10. Manner, Hans & Türk, Dennis & Eichler, Michael, 2016. "Modeling and forecasting multivariate electricity price spikes," Energy Economics, Elsevier, vol. 60(C), pages 255-265.
    11. Gianfreda, Angelica & Parisio, Lucia & Pelagatti, Matteo, 2016. "Revisiting long-run relations in power markets with high RES penetration," Energy Policy, Elsevier, vol. 94(C), pages 432-445.
    12. He, Kaijian & Yu, Lean & Tang, Ling, 2015. "Electricity price forecasting with a BED (Bivariate EMD Denoising) methodology," Energy, Elsevier, vol. 91(C), pages 601-609.

    More about this item

    Keywords

    Hidden Markov Regime Switching models; Electricity spot price; Independent Spike models; Market integration;

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • G1 - Financial Economics - - General Financial Markets
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy

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