Earthquake Insurance via CAT Bonds Utilizing Autoregressive Neural Networks and Active Faults.

Catastrophe (CAT) bonds necessitate a robust construction with regard to the estimated probability measure of their triggering parameter. This article concentrates on earthquakes as the primary natural catastrophe of concern. By leveraging the geometry of active faults for estimating default probability, we utilize seismic event information spanning up to 15,000 years in the past—thereby surpassing the restricted time range of available historical catalogs commonly used in other analyses, which typically cover only a few hundred years. This article introduces the design and pricing methodology of CAT bonds employing autoregressive neural networks, extending the standard VAR Nelson-Siegel model for yield curves. It presents a case study focused on the region of Greece, estimating that an additional spread of approximately 500 basis points over LIBOR constitutes the minimum premium required to attract an investor to undertake the associated risk. This premium could be absorbed by insured parties as an alternative to the conventional insurance process. [ABSTRACT FROM AUTHOR]