Non-parametric Models for Univariate Claim Severity Distributions - an approach using R

Catalina Bolance (), Montserrat Guillen and David Pitt ()
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Catalina Bolance: Department of Econometrics, Riskcenter-IREA, Universitat de Barcelona
David Pitt: Department of Applied Finance and Actuarial Studies, Macquarie University

No 2014-01, Working Papers from Universitat de Barcelona, UB Riskcenter

Abstract: This paper presents an analysis of motor vehicle insurance claims relating to vehicle damage and to associated medical expenses. We use univariate severity distributions esti- mated with non-parametric methods. The methods are implemented using the statistical package R. The nonparametric analysis presented involves kernel density estimation. We illustrate the benefits of applying transformations to data prior to employing kernel based methods. We use a log-transformation and an optimal transformation amongst a class of transformations that produces symmetry in the data. The central aim of this paper is to provide educators with material that can be used in the classroom to teach statistical estimation methods, goodness of fit analysis and importantly statistical computing in the context of insurance and risk management. To this end, we have included in the Appendix of this paper all the R code that has been used in the analysis so that readers, both students and educators, can fully explore the techniques described.

Keywords: Loss modeling; insurance; finance (search for similar items in EconPapers)
Pages: 34 pages
Date: 2014-02
New Economics Papers: this item is included in nep-ger, nep-ias and nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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http://www.ub.edu/rfa/research/WP/UBriskcenterWP201401.pdf First version, 2014 (application/pdf)

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Persistent link: https://EconPapers.repec.org/RePEc:bak:wpaper:201401

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