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

1. 1Model the CAT bond primary market as a graph using relational data
2. 2Apply the Relational Graph Convolutional Network (R-GCN) architecture to learn node and edge representations
3. 3Train the model on historical data to predict CAT bond spreads
4. 4Evaluate the performance of the CATNet framework using metrics such as mean absolute error or mean squared error

Quantitative analysts and data scientists on a team can benefit from this approach to improve their CAT bond pricing models, and software engineers can implement the proposed framework

💡 Geometric deep learning can effectively capture complex relational data in CAT bond markets

💡 Geometric deep learning for CAT bond spread prediction