Using an ensemble of flood catastrophe models to explore the interplay of loss variability and the catastrophe model calibration process

Catastrophe (cat) models are tools, typically used in the (re)insurance industry, that evaluate the risks to a given portfolio by modelling the impact of thousands of years of synthetic hazard events. Of particular interest to users is an evaluation of the low probability (tail) risks. This includes asking questions such as, “what is the worst loss event that will be exceeded, on average, every 200 years?” 

An assessment of tail risks is inherently uncertain. This is compounded by a large number of uncertain or free parameters throughout the modelling chain which may be set via expert (subjective) judgement or via a process of calibration. The calibration process would take a given portfolio with known historical losses and adjust some of the free parameters to match the historical losses. This process may be reframed as creating a structured ensemble of catastrophe models with a range of each of the free or uncertain parameters. The process would then compare the modelled losses from each of the ensemble members to the known historical record and select the model that best represents the historical losses. 

A major limitation of the ensemble approach to catastrophe model calibration is the short historical record from which to select the most representative model. This work uses a flood catastrophe model ensemble to explore the calibration process by creating a short synthetic loss record from a single ensemble member and examining the downstream effects of using this loss record for model selection.