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Forecasting Volatility of the Nordic Electricity Market an Application of the MSGARCH

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  • Muhammad Naeem

    (Mathematics & Computer Science Department, Modern College of Business and Science, Muscat 133, Oman
    UCP Business School, University of Central Punjab, Lahore 54782, Pakistan)

  • Hothefa Shaker Jassim

    (Mathematics & Computer Science Department, Modern College of Business and Science, Muscat 133, Oman)

  • Kashif Saleem

    (School of Business, University of Wollongong, Dubai P.O. Box 20183, United Arab Emirates)

  • Maham Fatima

    (UCP Business School, University of Central Punjab, Lahore 54782, Pakistan)

Abstract

This paper studies the volatility of electricity spot prices in the Nordic market (Sweden, Finland, Denmark, and Norway) under regime switching. Utilizing Markov-switching GARCH (Generalized Autoregressive Conditional Heteroskedasticity) models, we provide strong evidence of nonlinear regime shifts in the volatility dynamics of these prices. Using in-sample criteria, we find that regime-switching models have lower AIC (Akaike information criterion) than single-regime GARCH models. In addition, out-of-sample forecasts indicate that regime-switching GARCH models have superior Value-at-Risk (VaR) prediction ability relative to single-regime models, which is directly pertinent to risk management. These findings highlight the importance of incorporating regime shifts into volatility models for accurately assessing and mitigating risks associated with electricity price fluctuations in deregulated markets.

Suggested Citation

  • Muhammad Naeem & Hothefa Shaker Jassim & Kashif Saleem & Maham Fatima, 2025. "Forecasting Volatility of the Nordic Electricity Market an Application of the MSGARCH," Risks, MDPI, vol. 13(3), pages 1-19, March.
    • Handle: RePEc:gam:jrisks:v:13:y:2025:i:3:p:58-:d:1615928
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    References listed on IDEAS

    as
    1. Zhang, Yue-Jun & Zhang, Lu, 2015. "Interpreting the crude oil price movements: Evidence from the Markov regime switching model," Applied Energy, Elsevier, vol. 143(C), pages 96-109.
    2. Robert F. Engle & Simone Manganelli, 2004. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," Journal of Business & Economic Statistics, American Statistical Association, vol. 22, pages 367-381, October.
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