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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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    Cited by:

    1. Ciarreta, Aitor & Zarraga, Ainhoa, 2016. "Modeling realized volatility on the Spanish intra-day electricity market," Energy Economics, Elsevier, vol. 58(C), pages 152-163.
    2. 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.
    3. Lyu, Chenyan & Do, Hung Xuan & Nepal, Rabindra & Jamasb, Tooraj, 2024. "Volatility spillovers and carbon price in the Nordic wholesale electricity markets," Energy Economics, Elsevier, vol. 134(C).
    4. Hartner, Michael & Permoser, Andreas, 2018. "Through the valley: The impact of PV penetration levels on price volatility and resulting revenues for storage plants," Renewable Energy, Elsevier, vol. 115(C), pages 1184-1195.
    5. Emanuel Kohlscheen & Richhild Moessner, 2022. "Changing Electricity Markets: Quantifying the Price Effects of Greening the Energy Matrix," Papers 2208.14650, arXiv.org.
    6. Aitor Ciarreta & Ainhoa Zarraga, 2015. "Analysis of Mean and Volatility Price Transmissions in the MIBEL and EPEX Electricity Spot Markets," The Energy Journal, , vol. 36(4), pages 41-60, October.
    7. 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.
    8. Han, Lin & Kordzakhia, Nino & Trück, Stefan, 2020. "Volatility spillovers in Australian electricity markets," Energy Economics, Elsevier, vol. 90(C).
    9. Antonio Naimoli & Giuseppe Storti, 2021. "Forecasting Volatility and Tail Risk in Electricity Markets," JRFM, MDPI, vol. 14(7), pages 1-17, June.
    10. Ioannidis, Filippos & Kosmidou, Kyriaki & Savva, Christos & Theodossiou, Panayiotis, 2021. "Electricity pricing using a periodic GARCH model with conditional skewness and kurtosis components," Energy Economics, Elsevier, vol. 95(C).
    11. 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).
    12. Sherzod N. Tashpulatov, 2022. "Modeling Electricity Price Dynamics Using Flexible Distributions," Mathematics, MDPI, vol. 10(10), pages 1-15, May.
    13. Erdogdu, Erkan, 2016. "Asymmetric volatility in European day-ahead power markets: A comparative microeconomic analysis," Energy Economics, Elsevier, vol. 56(C), pages 398-409.
    14. 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).
    15. Sikorska-Pastuszka, Magdalena & Papież, Monika, 2023. "Dynamic volatility connectedness in the European electricity market," Energy Economics, Elsevier, vol. 127(PA).
    16. 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.
    17. 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.
    18. 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).
    19. 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.
    20. Sherzod N. Tashpulatov, 2021. "The Impact of Regulatory Reforms on Demand Weighted Average Prices," Mathematics, MDPI, vol. 9(10), pages 1-15, May.
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    22. 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.
    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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