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.
- Alternating conditional expectations
- Climate impact
- Dimensionality reduction
- Distribution tail comparison
- GARMA model
- Regularized least squares
- Weather extremes
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Benjamin M, Rigby RA, Stasinopoulos D (2003) Generalized autoregressive moving average models. J Am Stat Assoc 98:214–223
Breiman L, Friedman JH (1985) Estimating optimal transformations for multiple regression and correlation. J Am Stat Assoc 80:580–598
Cheng C, Li Q, Li G, Auld H (2012) Climate change and heavy rainfall-related water damage insurance claims and losses in Ontario, Canada. J Water Resour Prot 4:49–62
Curry L, Weaver A, Wiebe E (2012) Determining the impact of climate change on insurance risk and the global community. Phase I: climate phase indicators report sponsored by the American Academy of Actuaries’ Property/Casualty Extreme Events Committee, the Canadian Institute of Actuaries (CIA), the Casualty Actuarial Society (CAS), and the Society of Actuaries (SOA).
Environment Canada. Historical climate data. http://climate.weather.gc.ca/. Accessed 31 May 2014
Gupta PL, Gupta RC, Tripathi RC (1996) Analysis of zero-adjusted count data. Comput Stat Data Anal 23:207–218
Haug O, Dimakos X, Vårdal JF, Aldrin M, Meze-Hausken E (2011) Future building water loss projections posed by climate change. Scand Actuar J 1:1–20
Li WK (1994) Time series models based on generalized linear models: some further results. Biometrics 50:506–511
Scheel I, Ferkingstad E, Frigessi A, Haug O, Hinnerichsen M, Meze-Hausken E (2013) A Bayesian hierarchical model with spatial variable selection: the effect of weather on insurance claims. J R Stat Soc Ser A 62:85–100
Smith AB, Katz RW (2013) U.S. billion-dollar weather and climate disasters: data sources, trends, accuracy and biases. Nat. Hazards 67(2):387–410
Stasinopoulos DM, Rigby RA (2007) Generalized additive models for location scale and shape (GAMLSS) in R. J Stat Softw 23(7):1–46
Zeger SL, Qaqish B (1988) Markov regression models for time series: a quasi-likelihood approach. Biometrics 44:1019–1032
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
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-17219-4
Online ISBN: 978-3-319-17220-0