Modeling the financial impact of climate extremes in weather derivatives.

Extreme weather events are becoming more frequent and severe, creating growing financial risks for businesses and investors. This thesis develops a comprehensive framework for modeling the economic impact of climate extremes and their mitigation through weather derivatives. Using historical climate data combined with statistical and AI-based weather forecasting models, we estimate the probability and intensity of extreme events. Monte Carlo simulations are then applied to evaluate the pricing and effectiveness of different derivative structures under a range of climate scenarios. To account for the potential clustering and self-excitation of extreme events in a changing climate, we incorporate Poisson and Hawkes processes into the modeling framework, capturing the dynamic nature of event occurrence and its influence on derivative pricing. The results provide insights into the role of weather derivatives as a financial tool to enhance resilience in an era of climate uncertainty.