GCPM: A ?exible package to explore credit portfolio risk
AbstractIn this article we introduce the novel GCPM package, which represents a generalized credit portfolio model framework. The package includes two of the most popular mod- eling approaches in the banking industry namely the CreditRisk+ and the CreditMetrics model and allows to perform several sensitivity analysis with respect to distributional or functional assumptions. Therefore, besides the pure quanti?cation of credit portfolio risk, the package can be used to explore certain aspects of model risk individually for every arbitrary credit portfolio. In order to guarantee maximum ?exibility, most of the models utilize a Monte Carlo simulation, which is implemented in C++, to achieve the loss dis- tribution. Furthermore, the package also o?ers the possibilities to apply simple pooling techniques to speed up calculations for large portfolios as well as a general importance sample approach. The article concludes with a comprehensive example demonstrating the ?exibility of the package.
Basel Committee on Banking Supervision (2006). “International Convergence of Capital Mea- surement and Capital Standards: A Revised Framework Comprehensive Version.”
Board of Govenors of the Federal Reserve System (2011). “Guidance on Model Risk Manage- ment.” Technical report, Federal Reserve System.
Credit Suisse First Boston International (1997). CreditRisk+ A Credit Risk Man- agement Framework. URL http://www.csfb.com/institutional/research/assets/ creditrisk.pdf.
Crouhy M, Galai D, Mark R (2000). “A comparative analysis of current credit risk models.” Journal of Banking & Finance, 24(1), 59–117. URL http://www.sciencedirect.com/ science/article/pii/S0378426699000539.
Fischer M, Dietz C (2011/12). “Modeling Sector Correlations with CreditRisk+ : The Com- mon Background Vector model.” The Journal of Credit Risk, 7, 23–43.
Fischer M, Jakob K (2015). “Copula-Speciﬁc Credit Portfolio Modeling.” In Innovations in Quantitative Risk Management. Springer.
Fischer M, Kaufmann F (2014). “An analytic approach to
quantify the sensitivity of CreditRisk+ with respect to its underlying assumptions.” The Journal of Risk Model Vali- dation, 8(2), 23–37.
Fischer M, Mertel A (2012). “Quantifying Model Risk within a CreditRisk+ framework.” The Journal of Risk Model Validation, 6, 47–76.
Giese G (2003). “Enhancing CreditRisk+.” RISK, 16, 73–77.
Gordy MB (2000). “A comparative anatomy of credit risk models.” Journal of Banking & Finance, 24(1), 119–149. URL http://www.sciencedirect.com/science/article/pii/ S0378426699000540.
Gundlach VM (2003). “Basics of CreditRisk+.” In CreditRisk+ in the Banking Industry, chapter 2. Springer-Verlag Berlin Heidelberg.
Gupton GM, Finger CC, Bhatia M (1997). Creditmetrics: technical document. JP Morgan & Co. URL www.defaultrisk.com.
Haaf H, Reiss O, Schoenmakers J (2003). “Numerically Stable Computation of CreditRisk+.” In CreditRisk+ in the Banking Industry, chapter 5. Springer-Verlag Berlin Heidelberg.
Haaf H, Tasche D (2002). “Credit Portfolio Measurements.” GARP Risk Review, 7, 43–47.
Hamerle A, R¨osch D (2006). “Parameterizing Credit Risk Models.” Journal of Credit Risk, 2(4), 101–122. URL http://epub.uni-regensburg.de/8206.
Hofert M, Yan J, Maechler MM (2014). “Package Copula.” URL http://ie.archive. ubuntu.com/disk1/disk1/cran.r-project.org/web/packages/copula/copula.pdf.
Jakob K, Fischer M (2014). “Quantifying the impact of diﬀerent copulas in a generalized CreditRisk+ framework An empirical study.” Dependence Modeling, 2, 1–21. ISSN 2300- 2298. doi:10.2478/demo-2014-0001. URL http://www.degruyter.com/view/j/demo. 2014.2.issue/demo-2014-0001/demo-2014-0001.xml.
Joe H (1997). Multivariate Models and Dependence Concepts. Chapman & Hall/CRC.
Mai JF, Scherer M (2012). Simulating Copulas. Imperial College Press.
Merton RC (1974). “On the pricing of corporate debt: The risk structure of interest rates*.” The Journal of Finance, 29(2), 449–470. URL http://onlinelibrary.wiley.com/doi/ 10.1111/j.1540-6261.1974.tb03058.x/full.
Nelson RB (2006). An Introduction to Copulas. Springer Science+Business Media Inc.
Rubino G, Tuﬃn B (2009). Rare Event Simulation using Monte Carlo Methods. John Wiley & Sons Ltd Chichester, U.K.
Sklar A (1959). “Fonctions de r´epartition `a n dimensions et leurs marges.” Publ. Inst. Stat. Univ. Paris, 8, 229–231.
Wittmann A (2007). CreditMetrics: Functions for calculating the CreditMetrics risk model. R package version 0.0-2.
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