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

ArXiv ID: 2405.00697 “View on arXiv”

Authors: Unknown

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.

Keywords: Machine Learning, Catastrophe (CAT) Bonds, Nonlinear Pricing, Risk Factors, Asset Pricing, Insurance-Linked Securities (Catastrophe Bonds)

Complexity vs Empirical Score

• Math Complexity: 7.0/10
• Empirical Rigor: 8.5/10
• Quadrant: Holy Grail
• Why: The paper employs advanced machine learning techniques like XGBoost and conformal prediction, requiring sophisticated mathematics to model nonlinear dynamics and generate prediction intervals. It demonstrates high empirical rigor by using a substantial primary market dataset (1999–2021), comparing model performance with statistical metrics, and presenting results geared toward practical pricing applications.

  flowchart TD
    A["Research Goal<br>Price CAT bonds using ML"] --> B["Data Collection<br>1999-2021 Market Data"]
    B --> C["Methodology<br>Machine Learning Models"]
    C --> D{"Processing"}
    D --> E["Computational Analysis<br>Nonlinear Dynamics Discovery"]
    E --> F["Key Findings<br>ML Enhances Pricing Accuracy"]
    F --> G["Outcome<br>Better Risk Factor Integration"]
    G --> H["Implication<br>Improved Asset Pricing Models"]
    
    style A fill:#e1f5fe
    style B fill:#f3e5f5
    style C fill:#fff3e0
    style E fill:#e8f5e8
    style F fill:#fce4ec
    style G fill:#e8f5e8
    style H fill:#fff3e0