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Contributions from Spatial Models to Non-Life Insurance Pricing: An Empirical Application to Water Damage Risk

Author

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  • Maria Victoria Rivas-Lopez

    (Escuela Politécnica Superior, Universidad Francisco de Vitoria, 28223 Madrid, Spain
    International Doctorate in Economics, Universidad Nacional de Educación a Distancia (UNED), 28050 Madrid, Spain
    Business Administration Department, Universidad Internacional Villanueva, 28034 Madrid, Spain)

  • Roman Minguez-Salido

    (Department of Public Economy, Statistics and Economic Policy, University of Castilla-La Mancha, Avenida Los Alfares 44, 16071 Cuenca, Spain)

  • Mariano Matilla Garcia

    (Facultad de Económicas y Empresariales, Universidad Nacional de Educación a Distancia (UNED), 28050 Madrid, Spain)

  • Alejandro Echeverria Rey

    (Escuela Politécnica Superior, Universidad Francisco de Vitoria, 28223 Madrid, Spain)

Abstract

This paper explores the application of spatial models to non-life insurance data focused on the multi-risk home insurance branch. In the pricing modelling and rating process, spatial information should be considered by actuaries and insurance managers because frequencies and claim sizes may vary by region and the premium should be different considering this rating variable. In addition, it is relevant to examine the spatial dependence due to the fact that the frequency of claims in neighbouring regions is often expected to be more closely related than those in regions far from each other. In this paper, a comparison between spatial models, such as spatial autoregressive models (SAR), the spatial error model (SEM), and the spatial Durbin model (SDM), and a non-spatial model has been developed. The data used for this analysis are for a home insurance portfolio located in Spain, from which we have selected peril of water coverage.

Suggested Citation

  • Maria Victoria Rivas-Lopez & Roman Minguez-Salido & Mariano Matilla Garcia & Alejandro Echeverria Rey, 2021. "Contributions from Spatial Models to Non-Life Insurance Pricing: An Empirical Application to Water Damage Risk," Mathematics, MDPI, vol. 9(19), pages 1-12, October.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:19:p:2476-:d:649454
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    References listed on IDEAS

    as
    1. Joshua D. Woodard & Gary D. Schnitkey & Bruce J. Sherrick & Nancy Lozano‐Gracia & Luc Anselin, 2012. "A Spatial Econometric Analysis of Loss Experience in the U.S. Crop Insurance Program," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 79(1), pages 261-286, March.
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    4. Bivand, Roger & Piras, Gianfranco, 2015. "Comparing Implementations of Estimation Methods for Spatial Econometrics," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i18).
    5. Basile, Roberto & Durbán, María & Mínguez, Román & María Montero, Jose & Mur, Jesús, 2014. "Modeling regional economic dynamics: Spatial dependence, spatial heterogeneity and nonlinearities," Journal of Economic Dynamics and Control, Elsevier, vol. 48(C), pages 229-245.
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