Climate change will affect the insurance industry. We develop a Bayesian hierarchical statistical approach to explain and predict insurance losses due to weather events at a local geographical scale. The number of weather-related insurance claims is modelled combining generalized linear models with spatially smoothed variable selection. Using Gibbs sampling and reversible jump MCMC, the model is fitted on daily weather and insurance data from each of the 319 municipalities of southern and central Norway for the period 1997-2006.
Out-of-sample predictions from the model are very good. Our results show interesting regional patterns in the impact of different weather covariates. In addition to being useful for insurance pricing, our model can be used for short-term predictions based on weather forecasts and long-term predictions based on downscaled climate models. Using the model we discuss the vulnerability of Norwegian Insurance Companies, under various climate change scenarios.
Keywords: Climate change; Precipitation; Spatial models; Spatial variable selection
Biography: Arnoldo Frigessi is professor of statistics with the Department of Biostatistics at the University of Oslo. He has extensive and wide experiences both in interdisciplinary and methofdological research. He is the director of Statistics for Innovation, (SFI)2, a Norwegian centre of excellence for innovation. Frigessi is an elected member of the Norwegian Academy of Sciences. He is interested in computationally intensive inference for complex stochastic models, with applications in life sciences, climate change and insurance.