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Modeling the daily electricity price volatility with realized measures

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  • Frömmel, Michael
  • Han, Xing
  • Kratochvil, Stepan

Abstract

We propose using Realized GARCH-type models to estimate the daily price volatility in the EPEX power markets. The model specifications extract the volatility-related information from realized measures, which improves the in-sample fit of the data. More importantly, evidence on the out-of-sample predictability reinforces the value of the specifications, as the forecast quality is improved over the benchmark EGARCH model under eight conventional criteria. In particular, we show that the benefit of including intraday range as a realized measure is more substantial than realized variance. All the key findings are robust under rolling-window and recursive estimation schemes, Gaussian and skewed t-distribution assumptions on the innovation process, and alternative specifications on the predictable price component.

Suggested Citation

  • Frömmel, Michael & Han, Xing & Kratochvil, Stepan, 2014. "Modeling the daily electricity price volatility with realized measures," Energy Economics, Elsevier, vol. 44(C), pages 492-502.
  • Handle: RePEc:eee:eneeco:v:44:y:2014:i:c:p:492-502
    DOI: 10.1016/j.eneco.2014.03.001
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    5. Han, Lin & Kordzakhia, Nino & Trück, Stefan, 2020. "Volatility spillovers in Australian electricity markets," Energy Economics, Elsevier, vol. 90(C).
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    8. Sherzod N. Tashpulatov, 2022. "Modeling Electricity Price Dynamics Using Flexible Distributions," Mathematics, MDPI, vol. 10(10), pages 1-15, May.
    9. Erdogdu, Erkan, 2016. "Asymmetric volatility in European day-ahead power markets: A comparative microeconomic analysis," Energy Economics, Elsevier, vol. 56(C), pages 398-409.
    10. Sherzod N. Tashpulatov, 2018. "The Impact of Behavioral and Structural Remedies on Electricity Prices: The Case of the England and Wales Electricity Market," Energies, MDPI, vol. 11(12), pages 1-24, December.
    11. Li, Kun & Cursio, Joseph D. & Jiang, Mengfei & Liang, Xi, 2019. "The significance of calendar effects in the electricity market," Applied Energy, Elsevier, vol. 235(C), pages 487-494.
    12. Mwampashi, Muthe Mathias & Nikitopoulos, Christina Sklibosios & Konstandatos, Otto & Rai, Alan, 2021. "Wind generation and the dynamics of electricity prices in Australia," Energy Economics, Elsevier, vol. 103(C).
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    14. Sharma, Prateek & Vipul,, 2016. "Forecasting stock market volatility using Realized GARCH model: International evidence," The Quarterly Review of Economics and Finance, Elsevier, vol. 59(C), pages 222-230.
    15. Ciarreta, Aitor & Zarraga, Ainhoa, 2016. "Modeling realized volatility on the Spanish intra-day electricity market," Energy Economics, Elsevier, vol. 58(C), pages 152-163.
    16. Emanuel Kohlscheen & Richhild Moessner, 2022. "Changing Electricity Markets: Quantifying the Price Effects of Greening the Energy Matrix," Papers 2208.14650, arXiv.org.
    17. Prateek Sharma & Swati Sharma, 2015. "Forecasting gains of robust realized variance estimators: evidence from European stock markets," Economics Bulletin, AccessEcon, vol. 35(1), pages 61-69.
    18. Ma, Rufei & Liu, Zhenhua & Zhai, Pengxiang, 2022. "Does economic policy uncertainty drive volatility spillovers in electricity markets: Time and frequency evidence," Energy Economics, Elsevier, vol. 107(C).
    19. Mwampashi, Muthe Mathias & Nikitopoulos, Christina Sklibosios & Rai, Alan & Konstandatos, Otto, 2022. "Large-scale and rooftop solar generation in the NEM: A tale of two renewables strategies," Energy Economics, Elsevier, vol. 115(C).
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    23. Rintamäki, Tuomas & Siddiqui, Afzal S. & Salo, Ahti, 2017. "Does renewable energy generation decrease the volatility of electricity prices? An analysis of Denmark and Germany," Energy Economics, Elsevier, vol. 62(C), pages 270-282.

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    More about this item

    Keywords

    Volatility forecasting; Intraday range; Realized GARCH; Electricity;
    All these keywords.

    JEL classification:

    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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