  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.
