[1] Artzner, P., Delbaen, F., Eber, J.-M., and Heath, D. (1999). Coherent measures of risk. Math. Finance, 9:203-228.
Google Scholar
[2] BaFin. (2012). Erläuterung zu den MaRisk in der Fassung vom 14.12.2012, Dec 2012.
Google Scholar
[3] Barndorff-Nielsen, O. E. (1977). Exponentially decreasing distributions for the logarithm of particle size. Proc. R. Soc. A,
353:401-419.
Google Scholar
[4] Board of Governors of the Federal Reserve System. (2011). Supervisory guidance on model risk management. Letter 11-7.
http://www.federalreserve.gov
Google Scholar
[5] Dobric, J. and Schmid, F. (2005). Nonparametric estimation of the lower tail dependence in bivariate copulas. J. Appl. Stat.,
32:387-407.
CrossrefGoogle Scholar
[6] Ebmeyer, D., Klaas, R., and Quell, P. (2006). The role of copulas in the CreditRisk+ framework. In Copulas. Risk Books
London.
Google Scholar
[7] Fang, K.-T., Kotz, S., and Wang, K. (1990). Symmetric Multivariate and Related Distributions. Chapman & Hall/CRC London.
Google Scholar
[8] Fischer, M. and Dietz, C. (2011/12). Modeling sector correlations with CreditRisk+: The common background vector model.
The Journal of Credit Risk, 7:23-43.
Google Scholar
[9] Fischer, M. and Dörflinger, M. (2010). A note on a non-parametric tail dependence estimator. Far East J. Theor. Stat., 32:1-5.
Google Scholar
[10] Fischer, M. and Mertel, A. (2012). Quantifying model risk within a CreditRisk+ framework. The Journal of Risk Model Validation,
6:47-76.
Google Scholar
[11] Frey, R., McNeil, A.J., and Nyfeler, M.A. (2001). Copulas and credit models. RISK, October: 111-114.
Google Scholar
[12] Genest, C., Remillard, B., and Beaudoin, D. (2009). Goodness-of-t tests for copulas: A review and a power study. Insurance
Math. Econom., 44:199-213.
Web of ScienceGoogle Scholar
[13] Giese, G. (2003). Enhancing CreditRisk+. RISK, 16:73-77.
Google Scholar
[14] Gundlach, M. and Lehrbass, F. (2003). CreditRisk+ in the Banking Industry. Springer- Verlag Berlin Heidelberg.
Google Scholar
[15] Han, C. and Kang, J. (2008). An extended CreditRisk+ framework for portfolio credit risk management. The Journal of Credit
Risk, 4:63-80.
Google Scholar
[16] Hering, C., Hofert, M., Mai, J., and Scherer, M. (2010). Constructing hierarchical Archimedean copulas with Lévy subordinators.
J. Multivariate Anal., 101(6):1428-1433.
Google Scholar
[17] Hofert, M., Kojadinovic, I., Mächler, M., and Yan, J. (2012). copula: Multivariate Dependence with Copulas, R package version
0.999-5 edition. URL: http://CRAN. R-project.org/package=copula.
Google Scholar
[18] Jaworski, P., Durante, F., Härdle, W., and Rychlik, T. (2010). Copula Theory and Its Applications. Springer-Verlag Berlin
Heidelberg.
Google Scholar
[19] Joe, H. (1997). Multivariate Models and Dependence Concepts. Chapman & Hall/CRC London.
Google Scholar
[20] Li, D.X. (2000). On default correlation: A copula function approach. Journal of Fixed Income, 9:43-54.
Google Scholar
[21] Luethi, D. and Breymann, W. (2011). ghyp: A package on the generalized hyperbolic distribution and its special cases. URL:
http://CRAN.R-project.org/package= ghyp.
Google Scholar
[22] Mai, J.F. and Scherer, M. (2009). Bivariate extreme-value copula with discrete pickands dependence measure. Extremes,
14:311-324.
Google Scholar
[23] McNeil, A.J. (2008). Sampling nested Archimedean copulas. J. Stat. Comput. Simul., 78:567-581.
Google Scholar
[24] McNeil, A.J., Frey, R., and Embrechts, P. (2005). Quantitative Risk Management. Princeton University Press.
Google Scholar
[25] Merton, R.C. (1973). On the pricing of corporate debt: The risk structure of interest rates. Journal of Finance, 29:449-470.
Google Scholar
[26] Moschopoulos, P.G. (1985). The distribution of the sum of independendent gamma random variables. Ann. Inst. Statist.
Math., 37:541-544.
Google Scholar
[27] Nelsen, R.B. (2006). An Introduction to Copulas. Springer New York.
Google Scholar
[28] Oh, D.H. and Patton, A.J. (2012). Modelling dependence in high dimension with factor copulas. Manuscript, Duke University.
URL: http://public.econ.duke.edu/~ap172/Oh_Patton_MV_factor_copula_6dec12.pdf
Google Scholar
[29] Okhrin, O. and Ristig, A. (2012). Hierarchical Archimedean Copulae: The HAC Package. Humbold Universität Berlin. URL:
http://cran.r-project.org/web/ packages/HAC/index.html.
Google Scholar
[30] Okhrin, O., Okhrin, Y., and Schmid, W. (2013). Properties of hierarchical Archimedean copulas. Statistics & Risk Modeling,
30:21-54.
Google Scholar
[31] Paolella, M.S. (2007). Intermediate Probability: A Computational Approach. John Wiley & Sons Chichester.
Google Scholar
[32] Savu, C. and Trede, M. (2010). Hierarchical Archimedean Copulas. Quant. Finance, 10:295-304.
Google Scholar
[33] Sklar, A. (1959). Fonctions de répartition á n dimensions et leurs marges. Publ. Inst. Stat. Univ. Paris, 8:229-231.
Google Scholar
[34] Szpiro, G. (2009). Eine falsch angewendete Formel und ihre Folgen. Neue Züricher Zeitung, 18 März.
Google Scholar
[35] Wilde, T. (1997). CreditRisk+ A Credit Risk Management Framework. Working Paper, Credit Suisse First Boston.Google Scholar
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