Multi-period pricing of data breach catastrophe bonds: A hybrid triggers and LSTM framework

.This article further studies the problem of pricing data breaches employing catastrophe bonds. A multi-period pricing model incorporating a triggering mechanism that combines the monthly maximum breach size and the monthly number of data breach events is proposed. The copula method is used to model the complex interdependence between the monthly maximum of breach size and monthly number of data breach events. Furthermore, the Long Short-Term Memory model is used to capture the complex patterns behind financial market risks. A simulation study is carried out to illustrate the proposed pricing and cash-flowing mechanism. The study shows that when pricing a data breach catastrophe bond, the joint effect of the magnitude and frequency of the breach has a strong effect, and data-driven deep learning models have accurate performance.