Risk Aggregation by Using Copulas in Internal Models
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Abstract
According to the Solvency II directive the Solvency Capital Requirement (SCR) corresponds to the economic capital needed to limit the probability of ruin to 0.5%. This implies that (re-)insurance undertakings will have to identify their overall loss distributions. The standard approach of the mentioned Solvency II directive proposes the use of a correlation matrix for the aggregation of the single so-called risk modules respectively sub-modules. In our paper we will analyze the method of risk aggregation via the proposed application of correlations. We will find serious weaknesses, particularly concerning the recognition of extreme events, e. g. natural disasters, terrorist attacks etc. Even though the concept of copulas is not explicitly mentioned in the directive, there is still a possibility of applying it. It is clear that modeling dependencies with copulas would incur significant costs for smaller companies that might outbalance the resulting more precise picture of the risk situation of the insurer. However, incentives for those companies who use copulas, e. g. reduced solvency capital requirements compared to those who do not use it, could push the deployment of copulas in risk modeling in general.
- [1] European Commission, “Directive of the European Par- liament and of the Council on the Taking-Up and Pursuit of the Business of Insurance and Reinsurance,” 2009. http://register.consilium.europa.eu/pdf/en/09/st03/ st03643-re06.en09.pdf
- S. Wang, “Aggregation of Correlated Risk Portfolios: Models and Algorithms,” Proceedings of the Casualty Actuarial Society, Vol. 85, 1998, pp. 848-939.
- E. W. Frees and E. A. Valdez, “Understanding Relation- ships Using Copulas, North American Actuarial Journal, Vol. 2, No. 1, 1998, pp. 1-25.
- P. Blum, A. Dias and P. Embrechts, “The ART of De- pendence Modeling: The Latest Advances in Correlation Analysis,” In: M. Lane, Ed., Alternative Risk Strategies, Risk Books, London, 2002.
- A. J. McNeil, “Sampling Nested Archimedean Copulas,” Journal of Statistical Computation and Simulation, Vol. 78, No. 6, 2007, pp. 567-581. doi:10.1080/00949650701255834
- M. Eling and D. Toplek, “Modeling and Management of Nonlinear Dependencies-Copulas in Dynamic Financial Analysis,” Journal of Risk and Insurance, Vol. 76, No. 3, 2009, pp. 651-681. doi:10.1111/j.1539-6975.2009.01318.x
- A. Tang and E. A. Valdez, “Economic Capital and the Aggregation of Risks using Copulas,” 2006. http://www.ica2006.com/Papiers/282/282.pdf
- A. Patton, “Copula-Based Models for Financial Time Series,” In: T. G. Andersen, R. A Davies, J.-P. Kreiss, and T. Mikosch, Ed., Handbook of Financial Time Series, Springer, Berlin, 2009, pp. 767-785. doi:10.1007/978-3-540-71297-8_34
- C. Genest, M. Gendron and M. Bourdeau-Brien, “The Advent of Copulas,” European Journal of Finance, Vol. 15, No. 7-8, 2009, pp. 609-618. doi:10.1080/13518470802604457
- G. Szeg?, “Measures of Risk,” Journal of Banking and Finance, Vol. 26, No. 7, 2002, pp. 1253-1272. doi:10.1016/S0378-4266(02)00262-5
- D. Pfeifer, “M?glichkeiten und Grenzen der mathema- tischen Schadenmodellierung,” Zeitschrift für die gesam- te Versicherungswissenschaft—German Journal of Risk and Insurance, Vol. 92, No. 4, 2003, pp. 665-696.
- M. Sklar, “Fonctions de répartition à n dimensions et leurs marges,” Publications de l’Institut de Statistique de l’Un- iversité de Paris, No. 8, 1959, pp. 229-231.
- P. Embrechts, A. McNeil and D. Straumann, “Correlation and Dependence in Risk Management: Properties and Pitfalls,” 2002. http://www.math.ethz.ch/~strauman/ preprints/pitfalls.pdf