GCPM: A ?exible package to explore credit portfolio risk

In 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.

Quantifying the impact of different copulas in a generalized CreditRisk+ framework An empirical study

Without any doubt, credit risk is one of the most important risk types in the classical banking industry. Consequently, banks are required by supervisory audits to allocate economic capital to cover unexpected future credit losses. Typically, the amount of economical capital is determined with a credit portfolio model, e.g. using the popular CreditRisk+ framework (1997) or one of its recent generalizations (e.g. [8] or [15]). Relying on specific distributional assumptions, the credit loss distribution of the CreditRisk+ class can be determined analytically and in real time. With respect to the current regulatory requirements (see, e.g. [4, p. 9-16] or [2]), banks are also required to quantify how sensitive their models (and the resulting risk figures) are if fundamental assumptions are modified. Against this background, we focus on the impact of different dependence structures (between the counterparties of the bank’s portfolio) within a (generalized) CreditRisk+ framework which can be represented in terms of copulas. Concretely, we present some results on the unknown (implicit) copula of generalized CreditRisk+ models and quantify the effect of the choice of the copula (between economic sectors) on the risk figures for a hypothetical loan portfolio and a variety of parametric copulas.