Description Usage Arguments Value Author(s) References See Also Examples
The function helps to create a new object of class GCPM
. The arguments
of the function are passed to the object after performing some plausibility checks.
1 2 3  init(model.type = "CRP", link.function = "CRP", N, seed,
loss.unit, alpha.max = 0.9999, loss.thr = Inf, sec.var,
random.numbers = matrix(), LHR, max.entries=1e3)

model.type 
Character value, specifying the model type. One can choose between “simulative” and “CRP” which corresponds to the analytical version of the CreditRisk+ model (see First Boston Financial Products, 1997) 
link.function 
character value, specifying the type of the link function. One can choose
between “CRP”, which corresponds to \overline{PD}=PD\cdot (w^Tx)
and “CM” which corresponds to
\overline{PD}=Φ≤ft(\frac{Φ^{1}PDw^Tx}{√{1w^TΣ w}}\right),
where PD is the original PD from portfolio data, x is the vector of sector
drawings, Φ is the CDF of the standard normal distribution, w is the
vector of sector weights given in the portfolio data and Σ is the
correlation matrix of the sector variables estimated from 
N 
numeric value, defining the number of simulations if

seed 
numeric value used to initialize the random number generator. If 
loss.unit 
numeric positive value used to discretize potential losses. 
alpha.max 
numeric value between 0 and 1 defining the maximum CDFlevel which will be computed in case of an analytical CreditRisk+ type model. 
loss.thr 
numeric value specifying a lower bound for portfolio losses to be stored in
order to derive risk contributions on counterparty level. Using a lower value
needs a lot of memory but will be necessary in order to calculate risk
contributions on lower CDF levels. This parameter is used only if

sec.var 
named numeric vector defining the sector variances in case of a CreditRisk+
type model. The names have to correspond to the sector names given in the
portfolio. This parameter is used only if 
random.numbers 
matrix with sector drawings. The columns represent the sectors,
whereas the rows represent the scenarios (number of different simulations).
The column names must correspond to the names used in the portfolio data
(see 
LHR 
numeric vector of length equal to 
max.entries 
numeric value defining the maximum number of loss scenarios stored to calculate risk contributions. 
object of class GCPM
Kevin Jakob
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. "CreditMetricstechnical 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 021101301, 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:
Sumcheck (alpha = 0.999 ) VaRcont: 0.02% deviation
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