用图神经网络预测巨灾债券利差，揭示市场连接性影响定价。
CATNet: A geometric deep learning approach for CAT bond spread prediction in the primary market
- 将巨灾债券市场建模为图结构，用R-GCN捕捉复杂关系。
- 预测准确率显著优于随机森林与XGBoost基准模型。
- 揭示市场枢纽与风险集中度对价格的关键作用，适合风控与投资研究者。
传统巨灾债券定价模型难以捕捉其内在的复杂关联数据。本文提出CATNet，一种基于几何深度学习的框架，利用关系图卷积网络（R-GCN）将巨灾债券一级市场建模为图结构，挖掘其底层网络特性以预测利差。分析显示，巨灾债券市场具有无标度网络特征，少数高度连接的枢纽节点占据主导地位。CATNet在预测性能上显著优于强基准模型随机森林与XGBoost。可解释性分析证实，网络拓扑属性并非统计噪声，而是对发行人声誉、承销商影响力及风险集中度等行业经验的量化体现。研究表明，网络连通性是决定价格的关键因素，为风险评估提供了新范式，证明图模型兼具顶尖准确性与深层市场洞察力。
原文摘要 · Abstract (English)
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 higher predictive performance, significantly outperforming strong Random Forest and XGBoost benchmarks. Interpretability analysis confirms that the network's topological properties 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.
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