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

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.

Modeling risks from natural hazards with generalized additive models for location, scale and shape.

We investigate a new framework for estimating the frequency and severity of losses associated with catastrophic risks such as bushfires, storms and floods. We explore generalized additive models for location, scale and shape (GAMLSS) for the quantification of regional risk factors - geographical, weather and climate variables - with the aim of better quantifying the frequency and severity of catastrophic losses from natural perils. Due to the flexibility of the GAMLSS approach, we find a superior fit to empirical loss data for the applied models in comparison to generalized linear regression models typically applied in the literature. In particular the generalized beta distribution of the second kind (GB2) provides a good fit to the severity of losses. Including covariates in the calibration of the scale parameter, we obtain vastly differently shaped distributions for the predicted individual losses at different levels of the covariates. Testing the GAMLSS approach in an out-of-sample validation exercise, we also find support for a correct specification of the estimated models. More accurate models for the losses from natural hazards will help state and local government policy development, in particular for risk management and scenario planning for emergency services with respect to these perils.

Quantifying catastrophic and climate impacted hazards based on local expert opinions.

Quantifying the potential costs of catastrophic and climate impacted hazards is a challenging but important exercise as the occurrence of such events is usually associated with high damage and uncertainty. At the local level, there is often a lack of information on rare extreme events, which means that the available data is not sufficient to fit a distribution and derive parameter values for frequency and severity distributions. This paper discusses the use of local assessments of extreme events and utilises expert elicitation in order to obtain values for distribution parameters that will feed into management decisions with regards to quantifying catastrophic risks. We illustrate a simple approach, where a local expert is required to only specify two percentiles of the loss distribution in order to provide an estimate for the severity distribution of climate impacted hazards. In our approach we use heavy-tailed distributions to capture the severity of events. Our method allows local government decision makers to focus on extreme losses and the tail of the distribution. An illustration of the method is provided utilising an example that quantifies property losses from bushfires for a local area in northern Sydney. We further illustrate how key variables, such as discount rates, assumptions about climatic change and adaptation measures, will impact the estimates of losses.

Discounting the distant future-Data on Australian discount rates estimated by a stochastic interest rate model.

Data on certainty equivalent discount factors and discount rates for stochastic interest rates in Australia are provided in this paper. The data has been used for the analysis of investments into climate adaptation projects in ׳It׳s not now or never: Implications of investment timing and risk aversion on climate adaptation to extreme events׳ (Truong and Trück, 2016) [3] and can be used for other cost-benefit analysis studies in Australia. The data is of particular interest for the discounting of projects that create monetary costs and benefits in the distant future.