AssetCorr is a package for the estimation of intra and inter asset correlations within the Vasicek Portfolio Model. The implemented methods are:

intra correlation

- Asymptotic Method of Moments (“intraAMM”, Gordy (2000))
- Finite Sample Method of Moments (“intraFMM”, Gordy (2000))
- Joint Default Probability Matching 1 (“intraJDP1”, Lucas (1995))
- Joint Default Probability Matching 2 (“intraJDP2”, De Servigny and Renault (2002))
- Corrected Method of Moments (“intraCMM”, Frei and Wunsch (2018))
- Parametric Apporach- Mode Matching (“intraMode”, Botha and van Vuuren (2010))
- Parametric Apporach- Beta Distribution (“intraBeta”, Botha and van Vuuren (2010))
- Binomial Maximum Likelihood Estimator (“intraMLE”, Gordy and Heitfield (2010))
- Asymptotic Maximum Likelihood Estimator (“intraAMLE”, Duellmann and Gehde-Trapp (2004))

inter correlation

- Covariance Matching Estimator (“interCov”, Bluhm and Overbeck (2003))
- Joint Default Probability Matching (“interJDP”, De Servigny and Renault (2002))
- Corrected Method of Moments (“interCMM”, Frei and Wunsch (2018) )
- Binomial Maximum Likelihood Estimator (“interMLE”, Gordy and Heitfield (2010))
- Copula Based Maximum Likelihood Estimator (interCopula, Pfeuffer et al. (2018))

It is commonly known that these methods are exposed to an estimation bias, especially if the default time series is short, see for example Meyer (2009) or Duellmann et al.(2010). Furthermore, Loeffler (2003) has shown that wrongly estimated correlations have a huge impact on the resulting risk figures. To tackle this issue Pfeuffer et al. (2018) outlined the use of single and double bootstrap correction and additionally jackknife correction approaches to reduce the bias. The suggested correction methods and methods to infer confidence intervals are provided by the package AssetCorr.

To illustrate the bias of intra correlation methods, consider the following example:

Suppose we have a default time series with the following properties:

- Length: 20 observations(years)
- Number of obligors: 1000
- Probability of default: 0.01

The following plot displays on the x-axis the true assumed value of the intra asset correlation and on the y-axis the estimated intra asset correlation as the mean of 1000 simulations:

As we can see, the bias of method of moments methodologies can be tremendous, especially as the level of intra correlation increases. Furthermore, the MLE approach shows the best performance.

Due to this large bias, Pfeuffer et al. (2018) suggested to use bootstrap and jackknife correction to reduce the bias. The following plot illustrates the combination of MLE and single bootstrap correction:

As one can see, the bootstrap correction moves the original line towards the true value (red line), which implies that the estimation efficiency was improved. This is especially true for the range from 0 to 0.6, which covers the empirical estimated range.

In the following, the functionality of AssetCorr will be briefly illustrated using a hypothetical default data time series. Load the package and generate a hypothetical default time series with the implemented function:

```
library(AssetCorr)
set.seed(111)
#number of obligors: 1000
#intra asset correlation: 0.3
#length of the default time series: 20
#probability of default: 0.01
D1=defaultTimeseries(1000,0.3,20,0.01)
N1=rep(1000,20)
```

All methods for estimating the intra correlation have a very similar structure. Hence only one method will be used in the following. At the end of this section, another function will be illustrated which can be used to get an in depth analysis of the underlying default time series. First, the point estimate for the intra correlation will be estimated:

```
intraAMM(D1,N1)
```

```
## $Original
## [1] 0.2550277
```

As one can see, the Asymptotic Method of Moments underestimates the true value of 30% for the intra asset correlation. Hence Pfeuffer et al. (2018) suggested to use resampling methods to reduce this downward bias. Therefore, the single bootstrap correction will be illustrated in the following. This correction comes with the additional advantage that one can infer confidence intervals. Additionally, a plot of the bootstrap density can be generated:

```
Output<-intraAMM(D1,N1, B=1000, CI_Boot = 0.95, plot=TRUE)
```

```
Output$Original
```

```
## [1] 0.2550277
```

```
Output$Bootstrap
```

```
## [1] 0.2839424
```

```
Output$CI_Boot
```

```
## [1] 0.1487274 0.3588796
```

Furthermore, the double bootstrap correction achieves a higher order of bias reduction, but comes with a substantial increase in computational time. Hence the number of repetitions in the inner and outer loop is reduced to 100, which implies that the single bootstrap corrected estimate is now calculated with 100 repetitions (instead of 1000 as in the previous setting):

```
Output<-intraAMM(D1,N1, DB=c(100,100))
Output$Original
```

```
## [1] 0.2550277
```

```
Output$Bootstrap
```

```
## [1] 0.2900257
```

```
Output$Double_Bootstrap
```

```
## [1] 0.3019264
```

As one can see, the double bootstrap correction produces the closest estimate, compared to the true value of 0.3.

Additionally, a jackknife correction is implemented:

```
intraAMM(D1,N1, JC=TRUE)
```

```
## $Original
## [1] 0.2550277
##
## $Jackknife
## [1] 0.2981715
```

As an additional feature, the package AssetCorr provides the possibility to evaluate and visualize a default time series using a list of estimators simultaneously to obtain first insights. The default is set to use all intra correlation estimators:

```
Output<-intraALL(D1,N1, B=500, plot=TRUE,Adjust = 0.0001, Estimator = c("AMM","FMM","CMM","JDP1","JDP2","AMLE") )
```

```
Output
```

```
## Estimator Estimate Type correction B DB CI_Boot CI
## 1 AMM 0.2550277 PEST none NA NA NA
## 2 FMM 0.2513167 PEST none NA NA NA
## 3 CMM 0.2816997 PEST none NA NA NA
## 4 JDP1 0.2427684 PEST none NA NA NA
## 5 JDP2 0.2465936 PEST none NA NA NA
## 6 AMLE 0.2610108 PEST none NA NA NA
## 7 AMM 0.2818566 CEST Bootstrap 500 NA NA
## 8 FMM 0.2839108 CEST Bootstrap 500 NA NA
## 9 CMM 0.3219600 CEST Bootstrap 500 NA NA
## 10 JDP1 0.2739287 CEST Bootstrap 500 NA NA
## 11 JDP2 0.2745455 CEST Bootstrap 500 NA NA
## 12 AMLE 0.2738691 CEST Bootstrap 500 NA NA
```

Also the inter correlation estimates have a substantial impact on the portfolio's risk, various methods are implemented. First, two correlated default time series are generated.

```
library(mvtnorm)
set.seed(2)
#number of obligors: 1000
#intra asset correlation 1: 0.3
#intra asset correlation 2: 0.1
#inter correlation of the systematic factors: 0.5
#length of the default time series: 20
#probability of default: 0.01
Psi=rmvnorm(20,sigma=matrix(c(1,0.5,0.5,1),2))
PDcond1=pnorm((qnorm(0.01)-sqrt(0.3)*Psi[,1])/sqrt(1-0.3))
PDcond2=pnorm((qnorm(0.01)-sqrt(0.1)*Psi[,2])/sqrt(1-0.1))
D1=rbinom(20,1000,PDcond1)
D2=rbinom(20,1000,PDcond2)
N1=N2=rep(1000,20)
```

All methods for estimating the intra correlation have a very similar structure. Hence only one method will be used in the following. First, the point estimate for the inter correlation will be calculated. For this purpose, the intra correlation estimates of the default time series serve as input:

```
rho1=intraAMM(D1,N1)$Original
rho2=intraAMM(D2,N2)$Original
interCov(D1,N1,D2,N2,rho1,rho2)
```

```
## $Original
## [1] 0.4289773
```

Also for inter correlation methods the resampling corrections are implemented:

```
#Single bootstrap Correction
rho1=intraAMM(D1,N1)$Original
rho2=intraAMM(D2,N2)$Original
Output<- interCov(D1,N1,D2,N2,rho1,rho2, B=1000, CI_Boot = 0.95, plot=TRUE)
```

```
Output$Original
```

```
## [1] 0.4289773
```

```
Output$Bootstrap
```

```
## [1] 0.4579771
```

```
Output$CI_Boot
```

```
## [1] 0.08318413 0.73242436
```

```
#Double bootstrap correction
Output<- interCov(D1,N1,D2,N2,rho1,rho2, DB=c(100,100))
Output$Original
```

```
## [1] 0.4289773
```

```
Output$Bootstrap
```

```
## [1] 0.4648048
```

```
Output$Double_Bootstrap
```

```
## [1] 0.4675375
```

```
#Furthermore, a Jackknife correction would be possible
```

Similar to the estimation of the intra correlations, the AssetCorr package also provides a function to combine several estimation techniques simultaneously. This function gives the user an in depth review of the dependencies of two default time series.

```
#A general overview
Output<-interALL(D1,N1,D2,N2,rho1,rho2 ,B=100, plot=TRUE)
```

```
Output
```

```
## Estimator Estimate Type correction B DB CI_Boot CI
## 1 Copula 0.5236092 PEST none NA NA NA
## 2 Cov 0.4289773 PEST none NA NA NA
## 3 JDP 0.4113636 PEST none NA NA NA
## 4 MLE 0.4736568 PEST none NA NA NA
## 5 Copula 0.5511928 CEST Bootstrap 100 NA NA
## 6 Cov 0.4888167 CEST Bootstrap 100 NA NA
## 7 JDP 0.4354319 CEST Bootstrap 100 NA NA
## 8 MLE 0.5063055 CEST Bootstrap 100 NA NA
```

In practical applications, there are several default time series corresponding to several different sectors. To give the user a first indication of the sectors and their dependencies to other sectors, we provide a detailed analyze function. It estimates the intra and inter correlations for a portfolio using all or selected estimation methods. Suppose, we have three sectors within the portfolio of interest. All sectors have the same intra correlation of 30% and are correlated with an inter correlation of 50%. Similar to the previous review functions, the default is set to use all available estimators:

```
#A general overview
library(mvtnorm)
set.seed(111)
NoO=1000 #Number of obligors in each sector
Years=20
AC=0.3
PD=0.01
#Calculate the conditional PDs:
Psi=rmvnorm(Years,sigma=matrix(c(1,0.5,0.5,0.5,1,0.5,0.5,0.5,1),3))
PDcond1=pnorm((qnorm(PD)-sqrt(AC)*Psi[,1])/sqrt(1-AC))
PDcond2=pnorm((qnorm(PD)-sqrt(AC/2)*Psi[,2])/sqrt(1-AC/2))
PDcond3=pnorm((qnorm(PD)-sqrt(AC*2)*Psi[,3])/sqrt(1-AC*2))
#Draw the default time series, depending on the conditional PDs
DTS=cbind(rbinom(Years,NoO,PDcond1),rbinom(Years,NoO,PDcond2),rbinom(Years,NoO,PDcond3))
N=matrix(NoO,nrow = Years,ncol = 3)
Output<-analyze_AssetCorr(DTS,N, B=100, Intra = c("AMM","FMM","CMM","JDP1"), Inter=c("Copula","Cov","JDP"))
```

```
#Furthermore, the analyze_AssetCorr function also proves single/double bootstrap
#and Jackknife corrections. Additionally, the confidence intervals
#can be estimated and visualized.
```

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