Climate Data Improves Cat Bond Coupon Prediction
Published:Dec 27, 2025 17:19
•1 min read
•ArXiv
Analysis
This paper addresses a timely and important problem: predicting the pricing of catastrophe bonds, which are crucial for managing risk from natural disasters. The study's significance lies in its exploration of climate variability's impact on bond pricing, going beyond traditional factors. The use of machine learning and climate indicators offers a novel approach to improve predictive accuracy, potentially leading to more efficient risk transfer and better pricing of these financial instruments. The paper's contribution is in demonstrating the value of incorporating climate data into the pricing models.
Key Takeaways
- •Climate data significantly improves the accuracy of machine learning models for predicting catastrophe bond coupons.
- •Extremely randomized trees performed best among the tested machine learning algorithms.
- •The study highlights the importance of considering climate variability in financial risk assessment, particularly for instruments like CAT bonds.
Reference
“Including climate-related variables improves predictive accuracy across all models, with extremely randomized trees achieving the lowest root mean squared error (RMSE).”