English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

We focus on model risk and risk sensitivity when addressing the insurabilityof cyber risk. The standard statistical approaches to assessment ofinsurability and potential mispricing are enhanced in several aspects involvingconsideration of model risk. Model risk can arise from model uncertainty, andparameters uncertainty. We demonstrate how to quantify the effect of model riskin this analysis by incorporating various robust estimators for key modelparameter estimates that apply in both marginal and joint cyber risk lossprocess modelling. We contrast these robust techniques with standard methodspreviously used in studying insurabilty of cyber risk. This allows us toaccurately assess the critical impact that robust estimation can have on tailindex estimation for heavy tailed loss models, as well as the effect of robustdependence analysis when quantifying joint loss models and insurance portfoliodiversification. We argue that the choice of such methods is akin to a form ofmodel risk and we study the risk sensitivity that arise from choices relatingto the class of robust estimation adopted and the impact of the settingsassociated with such methods on key actuarial tasks such as premium calculationin cyber insurance. Through this analysis we are able to address the questionthat, to the best of our knowledge, no other study has investigated in thecontext of cyber risk: is model risk present in cyber risk data, and how doesis it translate into premium mispricing? We believe our findings shouldcomplement existing studies seeking to explore insurability of cyber losses. Inorder to ensure our findings are based on realistic industry informed lossdata, we have utilised one of the leading industry cyber loss datasets obtainedfrom Advisen, which represents a comprehensive data set on cyber monetarylosses, from which we form our analysis and conclusions.

Cyber Loss Model Risk Translates to Premium Mispricing and Risk Sensitivity