Abstract
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.
Highlights
Banks apply credit portfolio models in order to quantify the amount of economic capital which must be withheld in order to cover unexpected losses caused by credit defaults
Starting from the basic CreditRisk+ model, which is characterized by certain distributional assumptions, we show how risk figures might change if these assumptions are modified
Quantifying credit portfolio risk is an essential part of risk controlling of financial institutions
Summary
Banks apply credit portfolio models in order to quantify the amount of economic capital which must be withheld in order to cover unexpected losses caused by credit defaults. A great advantage of GCPM over other available packages for R (R Core Team 2014), like QRM (Pfaff and McNeil 2014) or CreditMetrics (Wittmann 2007), is that it utilizes an object oriented approach, where one object consists of a specified model together with all portfolio information and risk figures (once the portfolio loss distribution was estimated). We will provide several examples and demonstrate how already existing packages and basic R functions can be used to construct a parametrization (i.e. a sample from the multivariate sector distribution). For those who are interested in this topic, we refer to Hamerle and Rosch (2006). The available functions of the package are introduced including a simple pooling technique which will be useful for homogeneous portfolios (e.g. retail portfolios)
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