CATNet: A geometric deep learning approach for CAT bond spread prediction in the primary market

ArXiv ID: 2508.10208 “View on arXiv”

Authors: Dixon Domfeh, Saeid Safarveisi

Abstract

Traditional models for pricing catastrophe (CAT) bonds struggle to capture the complex, relational data inherent in these instruments. This paper introduces CATNet, a novel framework that applies a geometric deep learning architecture, the Relational Graph Convolutional Network (R-GCN), to model the CAT bond primary market as a graph, leveraging its underlying network structure for spread prediction. Our analysis reveals that the CAT bond market exhibits the characteristics of a scale-free network, a structure dominated by a few highly connected and influential hubs. CATNet demonstrates high predictive performance, significantly outperforming a strong Random Forest benchmark. The inclusion of topological centrality measures as features provides a further, significant boost in accuracy. Interpretability analysis confirms that these network features are not mere statistical artifacts; they are quantitative proxies for long-held industry intuition regarding issuer reputation, underwriter influence, and peril concentration. This research provides evidence that network connectivity is a key determinant of price, offering a new paradigm for risk assessment and proving that graph-based models can deliver both state-of-the-art accuracy and deeper, quantifiable market insights.

Keywords: Relational Graph Convolutional Network, Catastrophe bonds, Network topology, Spread prediction, Geometric deep learning, Catastrophe bonds (Insurance-linked securities)

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 6.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced geometric deep learning (R-GCN) with graph theory concepts like scale-free networks, indicating high mathematical complexity. It also demonstrates high empirical rigor through backtesting on a real-world dataset of 803 CAT bond contracts, outperforming a strong benchmark and providing interpretability analysis.

  flowchart TD
    A["Research Goal: Predict<br>CAT bond spreads using<br>network topology"] --> B["Data Collection & Graph Construction"]
    B --> C["Feature Engineering:<br>Topological Centrality Measures"]
    C --> D["Model Architecture:<br>Relational Graph Convolutional Network R-GCN"]
    D --> E["Computational Process:<br>Training & Learning<br>Network Embeddings"]
    E --> F["Key Findings/Outcomes"]
    
    B --> B1((Primary Market Data))
    B --> B2((Bond Characteristics))
    
    F --> F1["CATNet outperforms<br>Random Forest benchmark"]
    F --> F2["Market is Scale-Free<br>Network dominated by hubs"]
    F --> F3["Network features act as<br>quantitative proxies for<br>industry intuition"]