Description Details Author(s) References See Also Examples
The package helps to analyze the default risk of credit portfolios. Commonly known models, like CreditRisk+ or the CreditMetrics model are implemented in their very basic settings. The portfolio loss distribution can be achieved either by simulation or analytically in case of the classic CreditRisk+ model. Models are only implemented to respect losses caused by defaults, i.e. migration risk is not included. The package structure is kept flexible especially with respect to distributional assumptions in order to quantify the sensitivity of risk figures with respect to several assumptions. Therefore the package can be used to determine the credit risk of a given portfolio as well as to quantify model sensitivities.
Package: | GCPM |
Type: | Package |
Version: | 1.2.2 |
Date: | 2016-12-29 |
License: | GPL-2 |
Kevin Jakob
Maintainer: Kevin Jakob <Kevin.Jakob.Research@gmail.com>
Jakob, K. & Fischer, M. "GCPM: A flexible package to explore credit portfolio risk" Austrian Journal of Statistics 45.1 (2016): 25:44
Morgan, J. P. "CreditMetrics-technical document." JP Morgan, New York, 1997
First Boston Financial Products, "CreditRisk+", 1997
Gundlach & Lehrbass, "CreditRisk+ in the Banking Industry", Springer, 2003
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 | #create a random portfolio with NC counterparties
NC=100
#assign business lines and countries randomly
business.lines=c("A","B","C")
CP.business=business.lines[ceiling(runif(NC,0,length(business.lines)))]
countries=c("A","B","C","D","E")
CP.country=countries[ceiling(runif(NC,0,length(countries)))]
#create matrix with sector weights (CreditRisk+ setting)
#according to business lines
NS=length(business.lines)
W=matrix(0,nrow = NC,ncol = length(business.lines),
dimnames = list(1:NC,business.lines))
for(i in 1:NC){W[i,CP.business[i]]=1}
#create portfolio data frame
portfolio=data.frame(Number=1:NC,Name=paste("Name ",1:NC),Business=CP.business,
Country=CP.country,EAD=runif(NC,1e3,1e6),LGD=runif(NC),
PD=runif(NC,0,0.3),Default=rep("Bernoulli",NC),W)
#draw sector variances randomly
sec.var=runif(NS,0.5,1.5)
names(sec.var)=business.lines
#draw N sector realizations (independent gamma distributed sectors)
N=5e4
random.numbers=matrix(NA,ncol=NS,nrow=N,dimnames=list(1:N,business.lines))
for(i in 1:NS){
random.numbers[,i]=rgamma(N,shape = 1/sec.var[i],scale=sec.var[i])}
#create a portfolio model and analyze the portfolio
TestModel=init(model.type = "simulative",link.function = "CRP",N = N,
loss.unit = 1e3, random.numbers = random.numbers,LHR=rep(1,N),loss.thr=5e6,
max.entries=2e4)
TestModel=analyze(TestModel,portfolio)
#plot of pdf of portfolio loss (in million) with indicators for EL, VaR and ES
alpha=c(0.995,0.999)
plot(TestModel,1e6,alpha=alpha)
#calculate portfolio VaR and ES
VaR=VaR(TestModel,alpha)
ES=ES(TestModel,alpha)
#Calculate risk contributions to VaR and ES
risk.cont=cbind(VaR.cont(TestModel,alpha = alpha),
ES.cont(TestModel,alpha = alpha))
|
Generalized Credit Portfolio Model
Copyright (C) 2015 Kevin Jakob & Dr. Matthias Fischer
This program is free software; you can redistribute it and/or
modify it under the terms of the GNU General Public License
version 2 as published by the Free Software Foundation.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor,
Boston, MA 02110-1301, USA.
Importing portfolio data....
3 sectors ...
100 counterparties (0 removed due to EAD=0 (0), lgd=0 (0), pd<=0 (0) pd>=1 (0))
Portfolio statistics....
Loss unit: 1 K
Portfolio EAD:53.25 M
Portfolio potential loss:26.27 M
Portfolio expected loss:3.61 M(analytical)
Starting simulation (50000simulations )
0% 10 20 30 40 50 60 70 80 90 100%
|----|----|----|----|----|----|----|----|----|----|
**************************************************|
Simulation finished
Calculating loss distribution...
Calculating risk measures from loss distribution....
Expected loss from loss distribution: 3.54 M (deviation from EL calculated from portfolio data: -1.86%)
Exceedance Probability of the expected loss:0.4117
Portfolio mean expected loss exceedance: 5.96 M
Portfolio loss standard deviation:2.49 M
Deviation between VaR, EC contributions and VaR, EC caused by discontinuity of loss distribution:
Sum-check (alpha = 0.999 ) VaR-cont: -0.02% deviation
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