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Modeling realized volatility on the Spanish intra-day electricity market

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  • Ciarreta, Aitor
  • Zarraga, Ainhoa

Abstract

This paper models the realized volatility of the hourly prices from the six sessions of the Spanish intra-day electricity market for the period 2002–2014. Based on the sequential organization of the market, a model in which realized volatility depends on its own past and that of the other sessions is specified and then modified in two ways. On the one hand, total variation is decomposed into jump and non-jump components and on the other hand EGARCH innovations are considered. Estimation results show significant volatility transmissions between the sessions. Out-of-sample forecast criteria select EGARCH innovations for sessions 1 and 2, while simpler models with no EGARCH innovations and no jump distinction are preferred for sessions 5 and 6. We argue how results are driven by the market structure, the market design and the regulation of renewable generation.

Suggested Citation

  • Ciarreta, Aitor & Zarraga, Ainhoa, 2016. "Modeling realized volatility on the Spanish intra-day electricity market," Energy Economics, Elsevier, vol. 58(C), pages 152-163.
    • Handle: RePEc:eee:eneeco:v:58:y:2016:i:c:p:152-163
    DOI: 10.1016/j.eneco.2016.06.015
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    Cited by:

    1. Aitor Ciarreta & Peru Muniain & Ainhoa Zarraga, 2020. "Realized volatility and jump testing in the Japanese electricity spot market," Empirical Economics, Springer, vol. 58(3), pages 1143-1166, March.
    2. Ciarreta, Aitor & Pizarro-Irizar, Cristina & Zarraga, Ainhoa, 2020. "Renewable energy regulation and structural breaks: An empirical analysis of Spanish electricity price volatility," Energy Economics, Elsevier, vol. 88(C).
    3. Qu, Hui & Duan, Qingling & Niu, Mengyi, 2018. "Modeling the volatility of realized volatility to improve volatility forecasts in electricity markets," Energy Economics, Elsevier, vol. 74(C), pages 767-776.
    4. Zhu, Xuehong & Zhang, Hongwei & Zhong, Meirui, 2017. "Volatility forecasting using high frequency data: The role of after-hours information and leverage effects," Resources Policy, Elsevier, vol. 54(C), pages 58-70.
    5. Sherzod N. Tashpulatov, 2021. "Modeling and Estimating Volatility of Day-Ahead Electricity Prices," Mathematics, MDPI, vol. 9(7), pages 1-11, March.
    6. Abdullah, Mohammad & Abakah, Emmanuel Joel Aikins & Wali Ullah, G M & Tiwari, Aviral Kumar & Khan, Isma, 2023. "Tail risk contagion across electricity markets in crisis periods," Energy Economics, Elsevier, vol. 127(PB).
    7. Sirin, Selahattin Murat & Camadan, Ercument & Erten, Ibrahim Etem & Zhang, Alex Hongliang, 2023. "Market failure or politics? Understanding the motives behind regulatory actions to address surging electricity prices," Energy Policy, Elsevier, vol. 180(C).
    8. Chanatásig-Niza, Evelyn & Ciarreta, Aitor & Zarraga, Ainhoa, 2022. "A volatility spillover analysis with realized semi(co)variances in Australian electricity markets," Energy Economics, Elsevier, vol. 111(C).
    9. Aitor Ciarreta & Peru Muniainy & Ainhoa Zarraga, 2017. "Modelling Realized Volatility in Electricity Spot Prices: New insights and Application to the Japanese Electricity Market," ISER Discussion Paper 0991, Institute of Social and Economic Research, Osaka University.
    10. 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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    More about this item

    Keywords

    Realized volatility; Intra-day electricity market; HAR model; Jumps; EGARCH;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • Q49 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Other

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