Machine learning models for predicting catastrophe bond coupons using climate data

TL;DR

This paper demonstrates that incorporating climate variability indicators into machine learning models enhances the accuracy of predicting catastrophe bond coupons, highlighting climate's influence on financial risk modeling.

Contribution

It introduces new climate indicators into machine learning models for CAT bond pricing and compares their predictive performance, showing improved accuracy over traditional methods.

Findings

Climate variables improve model predictions

Extremely randomized trees achieve lowest RMSE

Climate variability influences CAT bond pricing

Abstract

In recent years, the growing frequency and severity of natural disasters have increased the need for effective tools to manage catastrophe risk. Catastrophe (CAT) bonds allow the transfer of part of this risk to investors, offering an alternative to traditional reinsurance. This paper examines the role of climate variability in CAT bond pricing and evaluates the predictive performance of various machine learning models in forecasting CAT bond coupons. We combine features typically used in the literature with a new set of climate indicators, including Oceanic Ni{\~n}o Index, Arctic Oscillation, North Atlantic Oscillation, Outgoing Longwave Radiation, Pacific-North American pattern, Pacific Decadal Oscillation, Southern Oscillation Index, and sea surface temperatures. We compare the performance of linear regression with several machine learning algorithms, such as random forest, gradient boosting, extremely randomized trees, and extreme gradient boosting. Our results show that including climate-related variables improves predictive accuracy across all models, with extremely randomized trees achieving the lowest root mean squared error (RMSE). These findings suggest that large-scale climate variability has a measurable influence on CAT bond pricing and that machine learning methods can effectively capture these complex relationships.