Abstract
The adverse effects of climate change bring increasingly more alterations to all aspects of human life and welfare, and one of the sectors that is particularly affected by changing climate is the insurance sector. Indeed, the year 2013 brought a record number of claims and substantial losses due to weather-related damages, and in the USA and Canada alone, the extreme weather events cost the insurance industry more than 3 billion dollars. The objective of this paper is to provide statistical data-driven insight on the (non)linear relationship between weather-related house insurance claims and atmospheric variables and to predict future claim dynamics accounting for changes in extreme precipitation. In this paper we propose to employ a flexible Generalized Autoregressive Moving Average (GARMA) model for count time series of claims, develop a new method to compare tails of the observed and projected extreme precipitation, and evaluate the impact of climate change on a number of house insurance claims in the GARMA framework. We illustrate our approach by studying insurance dynamics in four Canadian cities.
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Acknowledgements
Funding for the authors was provided by Natural Sciences and Engineering Research Council of Canada, Mitacs Canada, and Pioneer Natural Resources Undergraduate Research Program, USA.
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Soliman, M., Lyubchich, V., Gel, Y.R., Naser, D., Esterby, S. (2015). Evaluating the Impact of Climate Change on Dynamics of House Insurance Claims. In: Lakshmanan, V., Gilleland, E., McGovern, A., Tingley, M. (eds) Machine Learning and Data Mining Approaches to Climate Science. Springer, Cham. https://doi.org/10.1007/978-3-319-17220-0_16
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DOI: https://doi.org/10.1007/978-3-319-17220-0_16
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-17219-4
Online ISBN: 978-3-319-17220-0
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