Risk hedging against extreme weather through LLM-driven power insurance-futures portfolios

• Construct a prediction model that integrates physical mechanism constraints to achieve accurate forecasting of power shortages under extreme weather conditions and uncertainty quantification. • Achieve intelligent configuration of risk management tools through multi-source information fusion and semantic reasoning. • Empirical evidence from the German electricity market demonstrates that the portfolio strategy achieves a 94.8% risk coverage rate. Global climate change has led to frequent extreme weather events, exacerbating supply–demand imbalances and price volatility in power systems. Traditional risk management methods predominantly rely on statistical modeling and single financial instruments, making them inadequate for effectively addressing power shortages and electricity price risks under extreme scenarios. This paper proposes an intelligent financial hedging framework based on Large Language Models (LLMs) that synergistically applies insurance and futures portfolios to electricity markets under extreme weather conditions. First, we construct a CNN-LSTM-Attention prediction model that integrates physical mechanism constraints to achieve medium- to long-term forecasting of power shortages and uncertainty quantification. Second, we design an LLM-driven insurance-futures collaborative decision mechanism, which translates multi-source unstructured information (e.g., insurance clauses and market reports) into quantifiable strategy parameters through semantic understanding, thereby enabling adaptive optimization of insurance coverage and futures positions. Furthermore, we introduce a multi-objective Pareto optimization framework to achieve dynamic balance among risk coverage rate, cost-effectiveness, and return stability. Empirical results using the German electricity market as a case study demonstrate that the proposed framework achieves a 94.8% risk coverage rate under extreme high-temperature scenarios while significantly enhancing the economic efficiency and resilience of the strategy. The research indicates that LLMs can effectively bridge semantic risk identification with quantitative financial allocation, enhancing the intelligence of electricity risk hedging and providing a novel solution for market stability and energy transition under extreme weather.