Paper: arXiv 2509.25899

Authors: Julian Sester, Huansang Xu

Abstract

In this paper, we propose an alternative valuation approach for CAT bonds where a pricing formula is learned by deep neural networks. Once trained, these networks can be used to price CAT bonds as a function of inputs that reflect both the current market conditions and the specific features of the contract. This approach offers two main advantages. First, due to the expressive power of neural networks, the trained model enables fast and accurate evaluation of CAT bond prices. Second, because of its fast execution the trained neural network can be easily analyzed to study its sensitivities w.r.t. changes of the underlying market conditions offering valuable insights for risk management.

Complexity vs Empirical Score

  • Math Complexity: 6.5/10
  • Empirical Rigor: 6.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper presents a solid combination of rigorous stochastic calculus for CAT bond valuation (doubly stochastic Poisson processes, affine short-rate models) and modern deep learning techniques. By leveraging importance sampling to generate training data for neural networks, it provides an efficient surrogate model for fast sensitivity analysis and pricing. The exposition is clear and builds logically on established literature.

Research Flowchart

  flowchart TD
    A[Research Goal: Fast & Accurate CAT Bond Valuation] --> B[Input Data: Market Conditions & Contract Features]
    B --> C[Methodology: Train Deep Neural Networks as Function Approximators]
    C --> D[Computational Process: Learn Pricing Formula & Sensitivity Metrics]
    D --> E[Outcome 1: Fast & Accurate Real-Time CAT Bond Pricing]
    D --> F[Outcome 2: Efficient Risk Management & Sensitivity Analysis]