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An introduction to parametric and non-parametric models for bivariate positive insurance claim severity distributions

Author

Listed:
  • David Pitt

    (Dept. Economics, University of Melbourne)

  • Montserrat Guillén

    (Dept. Econometrics, University of Barcelona)

Abstract
We present a real data set of claims amounts where costs related to damage are recorded separately from those related to medical expenses. Only claims with positive costs are considered here. Two approaches to density estimation are presented: a classical parametric and a semi-parametric method, based on transformation kernel density estimation. We explore the data set with standard univariate methods. We also propose ways to select the bandwidth and transformation parameters in the univariate case based on Bayesian methods. We indicate how to compare the results of alternative methods both looking at the shape of the overall density domain and exploring the density estimates in the right tail.

Suggested Citation

  • David Pitt & Montserrat Guillén, 2010. "An introduction to parametric and non-parametric models for bivariate positive insurance claim severity distributions," Working Papers XREAP2010-03, Xarxa de Referència en Economia Aplicada (XREAP), revised Mar 2010.
    • Handle: RePEc:xrp:wpaper:xreap2010-03
    as

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    File URL: http://www.xreap.cat/RePEc/xrp/pdf/XREAP2010-3.pdf
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    File URL: http://www.xreap.cat/RePEc/xrp/pdf/XREAP2010-3.pdf
    File Function: Revised version, 2010
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    References listed on IDEAS

    as
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    Cited by:

    1. Ramon Alemany & Catalina Bolancé & Montserrat Guillén, 2012. "Nonparametric estimation of Value-at-Risk," Working Papers XREAP2012-19, Xarxa de Referència en Economia Aplicada (XREAP), revised Oct 2012.

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