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Why using a general model in Solvency II is not a good idea : an explanation from a Bayesian point of view

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  • Albarrán Lozano, Irene
  • Marín Díazaraque, Juan Miguel
  • Alonso, Pablo J.

Abstract

The passing of Directive 2009/138/CE (Solvency II) has opened a new era in the European insurance market. According to this new regulatory environment, the volume of own resources will be determined depending on the risks that any insurer would be holding. So, nowadays, the model to estimate the amount of economic capital is one of the most important elements. The Directive establishes that the European entities can use a general model to perform these tasks. However, this situation is far from being optimal because the calibration of the general model has been made using figures that reflects and average behaviour. This paper shows that not all the companies operating in a specific market has the same risk profile. For this reason, it is unsatisfactory to use a general model for all of them. We use the PAM clustering method and afterwards some Bayesian tools to check the results previously obtained. Analysed data (public information belonging to Spanish insurance companies about balance sheets and income statements from 1998 to 2007) comes from the DGSFP (Spanish insurance regulator).

Suggested Citation

  • Albarrán Lozano, Irene & Marín Díazaraque, Juan Miguel & Alonso, Pablo J., 2011. "Why using a general model in Solvency II is not a good idea : an explanation from a Bayesian point of view," DES - Working Papers. Statistics and Econometrics. WS ws113729, Universidad Carlos III de Madrid. Departamento de Estadística.
  • Handle: RePEc:cte:wsrepe:ws113729
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    1. Hadfield, Jarrod D., 2010. "MCMC Methods for Multi-Response Generalized Linear Mixed Models: The MCMCglmm R Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i02).
    2. Brockett, Patrick L. & Cooper, William W. & Golden, Linda L. & Rousseau, John J. & Wang, Yuying, 2004. "Evaluating solvency versus efficiency performance and different forms of organization and marketing in US property--liability insurance companies," European Journal of Operational Research, Elsevier, vol. 154(2), pages 492-514, April.
    3. Christophe Genolini & Bruno Falissard, 2010. "KmL: k-means for longitudinal data," Computational Statistics, Springer, vol. 25(2), pages 317-328, June.
    4. Van Gestel, Tony & Martens, David & Baesens, Bart & Feremans, Daniel & Huysmans, Johan & Vanthienen, Jan, 2007. "Forecasting and analyzing insurance companies' ratings," International Journal of Forecasting, Elsevier, vol. 23(3), pages 513-529.
    5. de Haan, Leo & Kakes, Jan, 2010. "Are non-risk based capital requirements for insurance companies binding?," Journal of Banking & Finance, Elsevier, vol. 34(7), pages 1618-1627, July.
    6. G. V. Kass, 1980. "An Exploratory Technique for Investigating Large Quantities of Categorical Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 29(2), pages 119-127, June.
    7. Constantin Anghelache & Dan Armeanu, 2008. "Application of Discriminant Analysis on Romanian Insurance Market," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania - AGER, vol. 11(11(528)), pages 51-62, November.
    8. McCarty, John A. & Hastak, Manoj, 2007. "Segmentation approaches in data-mining: A comparison of RFM, CHAID, and logistic regression," Journal of Business Research, Elsevier, vol. 60(6), pages 656-662, June.
    9. J. David Cummins & Mary A. Weiss, 1991. "The structure, conduct, and regulation of the property-liability insurance industry," Conference Series ; [Proceedings], Federal Reserve Bank of Boston, vol. 35, pages 117-164.
    10. Dedu, Vasile & Armeanu, Daniel & Enciu, Adrian, 2009. "Using the Multivariate Data Analysis Techniques on the Insurance Market," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 170-179, December.
    11. A. N. Pettitt & T. T. Tran & M. A. Haynes & J. L. Hay, 2006. "A Bayesian hierarchical model for categorical longitudinal data from a social survey of immigrants," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(1), pages 97-114, January.
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    Cited by:

    1. Eling, Martin & Pankoke, David, 2013. "Basis Risk, Procylicality, and Systemic Risk in the Solvency II Equity Risk Module," Working Papers on Finance 1306, University of St. Gallen, School of Finance.

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    Solvency II;

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