Evidence map›Paper›PMID 37207021›Full record

ArticleThe Geneva papers on risk and insurance. Issues and practice2023

Cyber loss model risk translates to premium mispricing and risk sensitivity.

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

Open access · hybridFull text read
PubMed Full text at PMC Publisher
In one paragraph

Article in The Geneva papers on risk and insurance. Issues and practice, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
1.3field-weighted citation impact, top 20% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 5 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 3 institutions in 3 countries.

Gareth W PetersStatistics & Applied Probability, University of California Santa Barbara, Santa Barbara, USA.ORCID 0000-0003-2768-8979
Matteo MalavasiActuarial Studies and Business Analytics, Macquarie University, Macquarie Park, Australia.
Georgy SofronovMathematical and Physical Sciences, Macquarie University, Macquarie Park, Australia.
Pavel V ShevchenkoActuarial Studies and Business Analytics, Macquarie University, Macquarie Park, Australia.
Stefan TrückActuarial Studies and Business Analytics, Macquarie University, Macquarie Park, Australia.
Jiwook JangActuarial Studies and Business Analytics, Macquarie University, Macquarie Park, Australia.
Macquarie University · AUSt Petersburg University · RUUniversity of California, Santa Barbara · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this paper we focus on model risk and risk sensitivity when addressing the insurability of cyber risk. The standard statistical approaches to assessment of insurability and potential mispricing are enhanced in several aspects involving consideration of model risk. Model risk can arise from model uncertainty and parameter uncertainty. We demonstrate how to quantify the effect of model risk in this analysis by incorporating various robust estimators for key model parameters that apply in both marginal and joint cyber risk loss process modelling. Through this analysis we are able to address the question that, to the best of our knowledge, no other study has investigated in the context of cyber risk: is model risk present in cyber risk data, and how does is it translate into premium mispricing? We believe our findings should complement existing studies seeking to explore the insurability of cyber losses.

Indexed as

Cyber insuranceCyber riskModel riskRisk sensitivityRobust dependence estimationRobust estimation

Identifiers

PMID37207021
PMCPMC10024527
OpenAlexW4226395594

What OpenQuestion holds

Textfull text, public
LicenceCC BY
reference markers read1
measurements read61
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.