%0 Journal Article
%T Pricing Cyber Security Insurance
%A Zhaoxin Lin
%A Travis R. A. Sapp
%A Rahul Parsa
%A Jackie Rees Ulmer
%A Chengxin Cao
%J Journal of Mathematical Finance
%P 46-70
%@ 2162-2442
%D 2022
%I Scientific Research Publishing
%R 10.4236/jmf.2022.121003
%X Cybersecurity breaches may be correlated due to geography, similar infrastructure, or use of a third-party contractor. We show how a logistic regression may be used to estimate the probability of an attack where breaches may be correlated among firms up and down the supply chain. We also show how a Poisson regression may be used to estimate the number of records breached. Losses arising from cybersecurity breaches have an unknown distribution. We propose the stock price reaction to a breach as an objective measure of the loss in wealth sustained by the firm due to a breach. This loss measure reflects the immediate and long-term effects of a breach, including reputational effects and other intangible impacts that are otherwise more difficult to quantify. We examine stock returns for 258 cybersecurity breach announcements over 2011-2016 in order to obtain the empirical loss distribution. We find a five-day abnormal return of -1.44%. Seventy-one percent of these 258 announcements result in a negative abnormal return, and a gamma distribution provides an excellent fit to these losses. In addition to introducing a predictive model for correlated losses, our study shows how insurers can use either the empirical stock return distribution of losses or the per record cost of a breach in the pricing of cyberinsurance.
%K Risk Management
%K Loss Distribution
%K Cyber Breach
%K Cyberinsurance
%K Insurance Premium
%K Correlated Losses
%K Event Study
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=114668