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

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.

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Journal
Risks
Published
2025-03-19
DOI
https://doi.org/10.3390/risks13030058
Primary Topic
Energy Load and Power Forecasting
Type
article
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article

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

Muhammad Waqar Naeem, Kashif Saleem, Maham Fatima, Hothefa Shaker Jassim
Risks
Energy Load and Power Forecasting
article

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

Muhammad Waqar Naeem, Kashif Saleem, Maham Fatima, Hothefa Shaker Jassim
article en

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.

RisksVol. 13(3)
University of Central Punjab (PK), Modern College of Business and Science (OM), University of Wollongong in Dubai (AE)
Openalex Percentile: Top 96%
Energy Load and Power Forecasting
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Forecasting Volatility of the Nordic Electricity Market an Application of the MSGARCH — Muhammad Waqar Naeem, Kashif Saleem, et al. · Risks (2025) | TGRS Research Map | TGRS