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Regime-switching models for electricity spot prices: Introducing heteroskedastic base regime dynamics and shifted spike distributions

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  • Janczura, Joanna
  • Weron, Rafal

Abstract

We calibrate Markov regime-switching (MRS) models to mean daily spot prices from the EEX market. Our empirical study shows that (i) models with shifted spike regime distributions lead to more realistic models of electricity spot prices and that (ii) introducing heteroskedasticity in the base regime leads to better spike identification and goodness-of-fit than in MRS models with the standard mean-reverting, constant volatility dynamics.

Suggested Citation

  • Janczura, Joanna & Weron, Rafal, 2009. "Regime-switching models for electricity spot prices: Introducing heteroskedastic base regime dynamics and shifted spike distributions," MPRA Paper 18784, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:18784
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    References listed on IDEAS

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    10. Rafał Weron, 2009. "Heavy-tails and regime-switching in electricity prices," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 69(3), pages 457-473, July.
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    1. repec:dui:wpaper:1502 is not listed on IDEAS
    2. Joanna Janczura & Rafal Weron, 2012. "Inference for Markov-regime switching models of electricity spot prices," HSC Research Reports HSC/12/01, Hugo Steinhaus Center, Wroclaw University of Technology.
    3. 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.
    4. Eichler, M. & Türk, D., 2013. "Fitting semiparametric Markov regime-switching models to electricity spot prices," Energy Economics, Elsevier, vol. 36(C), pages 614-624.
    5. Ida Bakke & Stein-Erik Fleten & Lars Ivar Hagfors & Verena Hagspiel & Beate Norheim & Sonja Wogrin, 2016. "Investment in electric energy storage under uncertainty: a real options approach," Computational Management Science, Springer, vol. 13(3), pages 483-500, July.
    6. Joanna Janczura & Rafał Weron, 2013. "Goodness-of-fit testing for the marginal distribution of regime-switching models with an application to electricity spot prices," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 97(3), pages 239-270, July.
    7. Eichler, M. & Türk, D.D.T., 2012. "Fitting semiparametric Markov regime-switching models to electricity spot prices," Research Memorandum 035, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    8. Niu, Shilei & Insley, Margaret, 2016. "An options pricing approach to ramping rate restrictions at hydro power plants," Journal of Economic Dynamics and Control, Elsevier, vol. 63(C), pages 25-52.
    9. Janczura, Joanna & Weron, Rafal, 2010. "An empirical comparison of alternate regime-switching models for electricity spot prices," Energy Economics, Elsevier, vol. 32(5), pages 1059-1073, September.
    10. 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.
    11. G P Girish & Aviral Kumar Tiwari, 2016. "A comparison of different univariate forecasting models forSpot Electricity Price in India," Economics Bulletin, AccessEcon, vol. 36(2), pages 1039-1057.
    12. Lisi, Francesco & Nan, Fany, 2014. "Component estimation for electricity prices: Procedures and comparisons," Energy Economics, Elsevier, vol. 44(C), pages 143-159.

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    More about this item

    Keywords

    regime-switching; heteroskedasticity; electricity spot price;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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