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Forecasting Volatility and Tail Risk in Electricity Markets

Author

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  • Antonio Naimoli

    (Dipartimento di Scienze Economiche e Statistiche (DISES), Università di Salerno, Via Giovannni Paolo II, 132, 84084 Fisciano, Italy)

  • Giuseppe Storti

    (Dipartimento di Scienze Economiche e Statistiche (DISES), Università di Salerno, Via Giovannni Paolo II, 132, 84084 Fisciano, Italy)

Abstract

This paper investigates the benefits of jointly using several realized measures in predicting daily price volatility, Value-at-Risk, and Expected Shortfall in the Australian electricity markets of New South Wales, Queensland, and Victoria. We propose using Realized GARCH-type models with multiple measurement equations based on robust estimators to account for market microstructure noise and jumps in electricity price series. The model specifications that combine information from multiple realized measures improve the in-sample fit of the data. The out-of-sample analysis shows that use of the jump-robust medRV estimator significantly increases the accuracy of volatility forecasts, while in forecasting Value-at-Risk and Expected Shortfall at different risk levels, the standard GARCH(1,1) also performs remarkably well.

Suggested Citation

  • Antonio Naimoli & Giuseppe Storti, 2021. "Forecasting Volatility and Tail Risk in Electricity Markets," JRFM, MDPI, vol. 14(7), pages 1-17, June.
    • Handle: RePEc:gam:jjrfmx:v:14:y:2021:i:7:p:294-:d:582734
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    References listed on IDEAS

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    3. Clift, Dean Holland & Stanley, Cameron & Hasan, Kazi N. & Rosengarten, Gary, 2023. "Assessment of advanced demand response value streams for water heaters in renewable-rich electricity markets," Energy, Elsevier, vol. 267(C).

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