Data breaches cause millions of dollars in financial losses each year. The insurance industry has been exploring the ways to transfer such extreme risk. In this work, we investigate data breach catastrophe (CAT) bonds via developing a multiperiod pricing model. It is found that the nonstationary extreme value model can capture the statistical pattern of the monthly maximum of data breach size very well and, in particular, a positive time trend is discovered. For the financial risks, data-driven time series approaches are proposed to model the complex patterns exhibited by the financial data, which are different from those in the literature. Simulation studies are performed to determine the bond prices and cash flows. Our results show that the data breach CAT bond can be an attractive financial product and an effective instrument for transferring the extreme data breach risk.

数据泄露每年造成数百万美元的经济损失。保险业一直在探索如何转移这种极端风险。在这项工作中，我们通过开发多期定价模型来调查数据泄露巨灾 (CAT) 债券。发现非平稳极值模型可以很好地捕捉数据泄露规模的月度最大值的统计模式，特别是发现了一个正的时间趋势。对于金融风险，提出了数据驱动的时间序列方法来模拟金融数据表现出的复杂模式，这与文献中的模式不同。进行模拟研究以确定债券价格和现金流量。