Cyber Risk Frequency, Severity and Insurance Viability

Matteo Malavasi, Gareth W. Peters, Pavel V. Shevchenko, Stefan Tr\"uck, Jiwook Jang and Georgy Sofronov
Additional contact information
Matteo Malavasi: Department of Actuarial Studies and Business Analytics, Macquarie University, Australia
Gareth W. Peters: Department of Statistics and Applied Probability, University of California Santa Barbara, USA
Pavel V. Shevchenko: Department of Actuarial Studies and Business Analytics, Macquarie University, Australia
Stefan Tr\"uck: Department of Actuarial Studies and Business Analytics, Macquarie University, Australia
Jiwook Jang: Department of Actuarial Studies and Business Analytics, Macquarie University, Australia
Georgy Sofronov: Department of Mathematics and Statistics, Macquarie University, Australia

Papers from arXiv.org

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 Generalised 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 modelling framework with premium calculated by classical certainty equivalence analysis utilising 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.

Date: 2021-11, Revised 2022-03
New Economics Papers: this item is included in nep-cwa, nep-ias and nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://arxiv.org/pdf/2111.03366 Latest version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2111.03366

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().