Cyber risk frequency, severity and insurance viability

Matteo Malavasi, Gareth W. Peters, Pavel V. Shevchenko, Stefan Trück, Jiwook Jang and Georgy Sofronov

Insurance: Mathematics and Economics, 2022, vol. 106, issue C, 90-114

Abstract: In this study an exploration of insurance risk transfer is undertaken for the cyber insurance industry in the United States of America, based on the leading industry dataset of cyber events provided by Advisen. We seek to address two core unresolved questions. First, what factors are the most significant covariates that may explain the frequency and severity of cyber loss events and are they heterogeneous over cyber risk categories? Second, is cyber risk insurable in regards to the required premiums, risk pool sizes and how would this decision vary with the insured companies industry sector and size? We address these questions through a combination of regression models based on the class of Generalized Additive Models for Location Shape and Scale (GAMLSS) and a class of ordinal regressions. These models will then form the basis for our analysis of frequency and severity of cyber risk loss processes. We investigate the viability of insurance for cyber risk using a utility modeling framework with premiums calculated by classical certainty equivalence analysis utilizing the developed regression models. Our results provide several new key insights into the nature of insurability of cyber risk and rigorously address the two insurance questions posed in a real data driven case study analysis.

Keywords: Cyber risk; GAMLSS; Cyber risk insurance; Ordinal regression (search for similar items in EconPapers)
JEL-codes: C51 C52 G22 G28 G32 L86 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:insuma:v:106:y:2022:i:c:p:90-114

DOI: 10.1016/j.insmatheco.2022.05.003

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