A Bayesian hierarchical model with spatial variable selection: the effect of weather on insurance claims

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 geographic scale. The number of weather-related insurance claims is modelled by combining generalized linear models with...

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Bibliographic Details
Published in:Applied statistics 2013-01, Vol.62 (1), p.85-100
Main Authors: Scheel, Ida, Ferkingstad, Egil, Frigessi, Arnoldo, Haug, Ola, Hinnerichsen, Mikkel, Meze-Hausken, Elisabeth
Format: Article
Language:English
Subjects:
A priori knowledge
Applications
Bayesian analysis
Bayesian method
Bayesian Poisson hurdle
Climate change
Climate models
Exact sciences and technology
Generalized linear models
Hierarchical models
Inference from stochastic processes
time series analysis
Insurance claims
Insurance companies
Insurance industry
Insurance, economics, finance
Linear models
Markov analysis
Markov processes
Markovian processes
Mathematics
Modeling
Monte Carlo simulation
Norway
Original
Precipitation
Probability and statistics
Probability theory and stochastic processes
Reliability, life testing, quality control
Sciences and techniques of general use
Snow
Spatial models
Spatial variable selection
Statistical methods
Statistics
Studies
Weather
Zero-altered Poisson model
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Summary: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 geographic scale. The number of weather-related insurance claims is modelled by combining generalized linear models with spatially smoothed variable selection. Using Gibbs sampling and reversible jump Markov chain Monte Carlo methods, this model is fitted on daily weather and insurance data from each of the 319 municipalities which constitute southern and central Norway for the period 1997—2006. Precise out-of-sample predictions validate the model. Our results show interesting regional patterns in the effect 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 for long-term predictions based on downscaled climate models.
ISSN:0035-9254
1467-9876
DOI:10.1111/j.1467-9876.2012.01039.x