Adjusting catastrophe model ensembles using importance sampling, with application to damage estimation for varying levels of hurricane activity. (21st October 2019) - Jewson, Stephen; Barnes, Clair; Cusack, Stephen; Bellone, Enrica
Abstract: Risk modellers in the insurance industry use catastrophe models to estimate the distribution of possible damage from natural catastrophes. The output from catastrophe models is often adjusted to create alternative risk scenarios. These adjustments are made for many reasons, such as to reflect different scientific hypotheses, different interpretations of historical data or different scenarios related to climate variability and climate change. Models that present the output in a list of simulated synthetic events with their associated damage (so‐called event loss tables) can be adjusted rather easily, since information about desired adjustments is typically expressed in terms of changes in the properties of events. Models that present the output in a list of simulated synthetic years (so‐called year loss tables) are harder to adjust, however, because the occurrences of the events are hard‐wired into the simulated years. A method is described that allows the adjustment of the results in a year loss table by the application of weights to the years. The weights are calculated in such a way as to capture the specified changes in properties of the underlying events. The method is demonstrated by applying it to output from a catastrophe model and using it to quantify the changes in US hurricane wind damage due to shifts between long‐term average, active and inactive levels of hurricane activity. It is shown that the method works well by comparing the results with more accurate results derived directly from the underlying event loss table. Abstract : Catastrophe models are complex computer models that estimate the possible financial impacts of natural catastrophes. A method is described that allows the output from such models to be adjusted, to quantify the impacts of differing modelling assumptions or the impacts of climate variability and climate change scenarios. The method is illustrated by considering how US hurricane wind damage varies as hurricane activity rates vary.