Time-varying systemic risk in electricity markets using generative adversarial networks: market resilience and policy
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2026-03
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Elsevier Ltd
Resumen
Current frameworks and policy instruments for monitoring and regulating European electricity markets fall short of fully addressing the complexities that arise during periods of market distress. Our study makes two key contributions in this area: First, it provides a novel, integrative analysis of systemic risk across 25 energy markets, encompassing oil, natural gas, and coal, as well as 21 European electricity markets. Second, it introduces Time Series Generative Adversarial Networks to systemic risk literature, enabling real-time tracking of market resilience. Our findings show that systemic distress in European electricity markets was higher in Q3 2021 than in late 2021 and early 2022, despite record-high electricity prices in the latter period, which many assumed reflected maximum market distress. This suggests that policy interventions enacted at the end of 2021 effectively reduced systemic distress in European electricity markets. However, fossil fuel markets reached a peak in risk during the first quarter of 2022, underscoring energy security concerns for Europe due to its reliance on foreign fuel sources, as prices are set in a global rather than regional context. Our modeling framework offers a tool to assess such risks in real time, providing valuable insights for proactive policymaking in the European energy sector.
Palabras clave
Artificial intelligence
Energy crisis
Energy prices
Systemic risk
TimeGAN
Energy crisis
Energy prices
Systemic risk
TimeGAN
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Materias
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Bohórquez Correa, S., Mosquera-López, S., & Uribe, J. M. (2026). Time-varying systemic risk in electricity markets using generative adversarial networks: market resilience and policy. Energy Policy, 210. https://doi.org/10.1016/J.ENPOL.2025.115034