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.

翻译：本文探讨了在巨灾（CAT）债券定价中应用机器学习模型的意义。通过整合先进的机器学习技术，我们的方法揭示了关键风险因素与CAT债券利差之间的非线性关系及复杂交互作用——这些动态特性常被传统的线性回归模型所忽略。基于1999年1月至2021年3月期间的一级市场CAT债券交易记录，我们的研究结果表明，机器学习模型不仅能提升CAT债券定价的准确性，还能更深入地揭示各类风险因素如何以非线性方式相互作用并影响债券价格。这些发现表明，投资者和发行方可通过引入机器学习技术，在CAT债券定价时更好地捕捉风险因素间错综复杂的相互作用。研究结果同时凸显了机器学习模型在完善我们对具有复杂风险结构的市场中资产定价机制理解的潜力。