Unveiling Nonlinear Dynamics in Catastrophe Bond Pricing: A Machine Learning Perspective

TL;DR

This paper demonstrates that machine learning models can effectively capture nonlinear relationships in catastrophe bond pricing, leading to improved accuracy and deeper risk factor insights compared to traditional methods.

Contribution

It introduces a machine learning approach to uncover nonlinear dynamics in CAT bond pricing, enhancing understanding and prediction accuracy over traditional linear models.

Findings

Machine learning models improve CAT bond pricing accuracy.

Nonlinear relationships between risk factors and bond spreads are uncovered.

Deeper insights into risk interactions benefit investors and issuers.

Abstract

This paper explores the implications of using machine learning models in the pricing of catastrophe (CAT) bonds. By integrating advanced machine learning techniques, our approach uncovers nonlinear relationships and complex interactions between key risk factors and CAT bond spreads -- dynamics that are often overlooked by traditional linear regression models. Using primary market CAT bond transaction records between January 1999 and March 2021, our findings demonstrate that machine learning models not only enhance the accuracy of CAT bond pricing but also provide a deeper understanding of how various risk factors interact and influence bond prices in a nonlinear way. These findings suggest that investors and issuers can benefit from incorporating machine learning to better capture the intricate interplay between risk factors when pricing CAT bonds. The results also highlight the potential for machine learning models to refine our understanding of asset pricing in markets characterized by complex risk structures.