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Signs of dependence and heavy tails in non-life insurance data

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  • Jonas Alm

Abstract

In this paper we study data from the yearly reports the four major Swedish non-life insurers have sent to the Swedish Financial Supervisory Authority (FSA). We aim at finding marginal distributions of, and dependence between, losses on the five largest lines of business (LoBs) in order to create models for Solvency Capital Requirement (SCR) calculation. We try to use data in an optimal way by sensibly defining an accounting year loss in terms of actuarial liability predictions, and by pooling observations from several companies when possible to decrease the uncertainty about the underlying distributions and their parameters. We find that dependence between LoBs is weaker in our data than what is assumed in the Solvency II standard formula. We also find dependence between companies that may affect financial stability, and must be taken into account when estimating loss distribution parameters. Moreover, we discuss under what circumstances an insurer is better (or worse) off using an internal model for SCR calculation instead of the standard formula.

Suggested Citation

  • Jonas Alm, 2015. "Signs of dependence and heavy tails in non-life insurance data," Papers 1501.00833, arXiv.org.
    • Handle: RePEc:arx:papers:1501.00833
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    References listed on IDEAS

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    1. Embrechts, Paul & Puccetti, Giovanni & Rüschendorf, Ludger, 2013. "Model uncertainty and VaR aggregation," Journal of Banking & Finance, Elsevier, vol. 37(8), pages 2750-2764.
    2. Merz, Michael & Wüthrich, Mario V. & Hashorva, Enkelejd, 2013. "Dependence modelling in multivariate claims run-off triangles," Annals of Actuarial Science, Cambridge University Press, vol. 7(1), pages 3-25, March.
    3. Diers, Dorothea & Eling, Martin & Marek, Sebastian D., 2012. "Dependence modeling in non-life insurance using the Bernstein copula," Insurance: Mathematics and Economics, Elsevier, vol. 50(3), pages 430-436.
    4. Lee, Simon C.K. & Lin, X. Sheldon, 2012. "Modeling Dependent Risks with Multivariate Erlang Mixtures," ASTIN Bulletin, Cambridge University Press, vol. 42(1), pages 153-180, May.
    5. Bernard, Carole & Jiang, Xiao & Wang, Ruodu, 2014. "Risk aggregation with dependence uncertainty," Insurance: Mathematics and Economics, Elsevier, vol. 54(C), pages 93-108.
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