Deep learning CAT bond valuation

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.

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