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Impact of dependence modeling of non-life insurance risks on capital requirement: D-Vine Copula approach

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  • Mejdoub, Hanène
  • Ben Arab, Mounira

Abstract

The purpose of this paper is to provide an extension to recent contributions in the field of quantitative risk management by modeling non-life insurance risks in a multivariate framework. This contribution examines the impact of explicit dependence modeling among non-life insurance losses on capital requirement. First, we focus on the modeling of dependence structure using copulas when the losses from the different business lines are dependent in some sense. Second, we concentrate on Value-at-Risk and Tail-Value-at-Risk as popular risk measures combined with D-Vine copulas model for the total risk capital estimates. For copula calibration, we use claims data from four lines of business of a Tunisian insurance company. Finally, we have conducted a comparative study of different methods under the two hypotheses of dependency and independency. Using Monte-Carlo simulation, our results reveal the advantages of D-Vine copula in modeling inhomogeneous structures of dependency due to its flexibility of use in a simulation context.

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  • Mejdoub, Hanène & Ben Arab, Mounira, 2018. "Impact of dependence modeling of non-life insurance risks on capital requirement: D-Vine Copula approach," Research in International Business and Finance, Elsevier, vol. 45(C), pages 208-218.
    • Handle: RePEc:eee:riibaf:v:45:y:2018:i:c:p:208-218
    DOI: 10.1016/j.ribaf.2017.07.152
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    1. Pourkhanali, Armin & Kim, Jong-Min & Tafakori, Laleh & Fard, Farzad Alavi, 2016. "Measuring systemic risk using vine-copula," Economic Modelling, Elsevier, vol. 53(C), pages 63-74.
    2. Grundke, Peter, 2010. "Top-down approaches for integrated risk management: How accurate are they?," European Journal of Operational Research, Elsevier, vol. 203(3), pages 662-672, June.
    3. Martin Eling & Denis Toplek, 2009. "Modeling and Management of Nonlinear Dependencies–Copulas in Dynamic Financial Analysis," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 76(3), pages 651-681, September.
    4. Robert C. Merton & André Perold, 1993. "Theory Of Risk Capital In Financial Firms," Journal of Applied Corporate Finance, Morgan Stanley, vol. 6(3), pages 16-32, September.
    5. Hato Schmeiser & Caroline Siegel & Joël Wagner, 2012. "The risk of model misspecification and its impact on solvency measurement in the insurance sector," Journal of Risk Finance, Emerald Group Publishing, vol. 13(4), pages 285-308, August.
    6. Guillén, Montserrat & Sarabia, José María & Prieto, Faustino, 2013. "Simple risk measure calculations for sums of positive random variables," Insurance: Mathematics and Economics, Elsevier, vol. 53(1), pages 273-280.
    7. Jianping Li & Xiaoqian Zhu & Cheng-Few Lee & Dengsheng Wu & Jichuang Feng & Yong Shi, 2015. "On the aggregation of credit, market and operational risks," Review of Quantitative Finance and Accounting, Springer, vol. 44(1), pages 161-189, January.
    8. Rosenberg, Joshua V. & Schuermann, Til, 2006. "A general approach to integrated risk management with skewed, fat-tailed risks," Journal of Financial Economics, Elsevier, vol. 79(3), pages 569-614, March.
    9. Genest, Christian & Rémillard, Bruno & Beaudoin, David, 2009. "Goodness-of-fit tests for copulas: A review and a power study," Insurance: Mathematics and Economics, Elsevier, vol. 44(2), pages 199-213, April.
    10. Aas, Kjersti & Czado, Claudia & Frigessi, Arnoldo & Bakken, Henrik, 2009. "Pair-copula constructions of multiple dependence," Insurance: Mathematics and Economics, Elsevier, vol. 44(2), pages 182-198, April.
    11. Brechmann, Eike Christian & Schepsmeier, Ulf, 2013. "Modeling Dependence with C- and D-Vine Copulas: The R Package CDVine," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 52(i03).
    12. Brechmann, Eike & Czado, Claudia & Paterlini, Sandra, 2014. "Flexible dependence modeling of operational risk losses and its impact on total capital requirements," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 271-285.
    13. 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.
    14. Dominique Guegan & Bertrand Hassani, 2013. "Multivariate VaRs for operational risk capital computation: a vine structure approach," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00645778, HAL.
    15. Christian Genest & Jean‐François Quessy & Bruno Rémillard, 2006. "Goodness‐of‐fit Procedures for Copula Models Based on the Probability Integral Transformation," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 33(2), pages 337-366, June.
    16. Arbenz, Philipp & Hummel, Christoph & Mainik, Georg, 2012. "Copula based hierarchical risk aggregation through sample reordering," Insurance: Mathematics and Economics, Elsevier, vol. 51(1), pages 122-133.
    17. Dißmann, J. & Brechmann, E.C. & Czado, C. & Kurowicka, D., 2013. "Selecting and estimating regular vine copulae and application to financial returns," Computational Statistics & Data Analysis, Elsevier, vol. 59(C), pages 52-69.
    18. Dominique Guegan & Bertrand Hassani, 2013. "Multivariate VaRs for operational risk capital computation: a vine structure approach," Post-Print halshs-00645778, HAL.
    19. Brechmann, Eike C. & Joe, Harry, 2015. "Truncation of vine copulas using fit indices," Journal of Multivariate Analysis, Elsevier, vol. 138(C), pages 19-33.
    20. Cossette, Hélène & Côté, Marie-Pier & Marceau, Etienne & Moutanabbir, Khouzeima, 2013. "Multivariate distribution defined with Farlie–Gumbel–Morgenstern copula and mixed Erlang marginals: Aggregation and capital allocation," Insurance: Mathematics and Economics, Elsevier, vol. 52(3), pages 560-572.
    21. Dominique Guegan & Bertrand Hassani, 2013. "Multivariate VaRs for operational risk capital computation: a vine structure approach," PSE-Ecole d'économie de Paris (Postprint) halshs-00645778, HAL.
    22. Jan Dhaene & Andreas Tsanakas & Emiliano A. Valdez & Steven Vanduffel, 2012. "Optimal Capital Allocation Principles," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 79(1), pages 1-28, March.
    23. Zhao, XiaoBing & Zhou, Xian, 2010. "Applying copula models to individual claim loss reserving methods," Insurance: Mathematics and Economics, Elsevier, vol. 46(2), pages 290-299, April.
    24. Lluís Bermúdez & Antoni Ferri & Montserrat Guillén, 2014. "On the use of risk measures in solvency capital estimation," International Journal of Business Continuity and Risk Management, Inderscience Enterprises Ltd, vol. 5(1), pages 4-13.
    25. Bernard, Carole & Jiang, Xiao & Wang, Ruodu, 2014. "Risk aggregation with dependence uncertainty," Insurance: Mathematics and Economics, Elsevier, vol. 54(C), pages 93-108.
    26. Fang, Y. & Madsen, L., 2013. "Modified Gaussian pseudo-copula: Applications in insurance and finance," Insurance: Mathematics and Economics, Elsevier, vol. 53(1), pages 292-301.
    27. Panjer, Harry H., 1981. "Recursive Evaluation of a Family of Compound Distributions," ASTIN Bulletin, Cambridge University Press, vol. 12(1), pages 22-26, June.
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