CDVineSim: Simulation from C- and D-vine copula models

In CDVine: Statistical Inference of C- And D-Vine Copulas

Description

This function simulates from given C- and D-vine copula models.

Usage

CDVineSim(N, family, par, par2=rep(0,length(family)), type)

Arguments

N	Number of d-dimensional observations simulated.
family	A d*(d-1)/2 integer vector of C-/D-vine pair-copula families with values 0 = independence copula 1 = Gaussian copula 2 = Student t copula (t-copula) 3 = Clayton copula 4 = Gumbel copula 5 = Frank copula 6 = Joe copula 7 = BB1 copula 8 = BB6 copula 9 = BB7 copula 10 = BB8 copula 13 = rotated Clayton copula (180 degrees; “survival Clayton”) 14 = rotated Gumbel copula (180 degrees; “survival Gumbel”) 16 = rotated Joe copula (180 degrees; “survival Joe”) 17 = rotated BB1 copula (180 degrees; “survival BB1”) 18 = rotated BB6 copula (180 degrees; “survival BB6”) 19 = rotated BB7 copula (180 degrees; “survival BB7”) 20 = rotated BB8 copula (180 degrees; “survival BB8”) 23 = rotated Clayton copula (90 degrees) 24 = rotated Gumbel copula (90 degrees) 26 = rotated Joe copula (90 degrees) 27 = rotated BB1 copula (90 degrees) 28 = rotated BB6 copula (90 degrees) 29 = rotated BB7 copula (90 degrees) 30 = rotated BB8 copula (90 degrees) 33 = rotated Clayton copula (270 degrees) 34 = rotated Gumbel copula (270 degrees) 36 = rotated Joe copula (270 degrees) 37 = rotated BB1 copula (270 degrees) 38 = rotated BB6 copula (270 degrees) 39 = rotated BB7 copula (270 degrees) 40 = rotated BB8 copula (270 degrees)
par	A d*(d-1)/2 vector of pair-copula parameters.
par2	A d*(d-1)/2 vector of second parameters for pair-copula families with two parameters (t, BB1, BB6, BB7, BB8; default: par2 = rep(0,length(family))).
type	Type of the vine model: 1 or "CVine" = C-vine 2 or "DVine" = D-vine

Value

An N x d matrix of data simulated from the given C- or D-vine copula model.

Author(s)

Carlos Almeida, Ulf Schepsmeier, Eike Brechmann, Jakob Stoeber

References

Aas, K., C. Czado, A. Frigessi, and H. Bakken (2009). Pair-copula constructions of multiple dependence. Insurance: Mathematics and Economics 44 (2), 182-198.

See Also

CDVineLogLik, BiCopSim

Examples

## Example 1: simulate from a 6-dimensional C-vine model 
## with Gaussian pair-copulas
d = 6
dd = d*(d-1)/2
fam1 = rep(1,dd)
par1 = c(0.2,0.69,0.73,0.22,-0.09,0.51,0.32,0.01,0.82,0.01,
         -0.2,-0.32,-0.19,-0.17,-0.06)
N = 100
U1 = CDVineSim(N,fam1,par1,type=1)
head(U1)

# calculate the log-likelihood
logLik = CDVineLogLik(U1,fam1,par1,type=1)
logLik$loglik


## Example 2: simulate from a 6-dimensional C-vine model 
## with Student t pair-copulas each with three degrees of freedom
fam2 = rep(2,dd)
par2 = rep(0.5,dd)
nu2 = rep(3,dd)
U2 = CDVineSim(N,fam2,par2,nu2,type=1)

# calculate the log-likelihood
logLik2 = CDVineLogLik(U2,fam2,par2,nu2,type=1)
logLik2$loglik


## Example 3: simulate from a 6-dimensional D-vine model
## with Student t pair-copulas each with five degrees of freedom
fam3 = rep(2,dd)
par3 = rep(0.4,dd)
nu3 = rep(5,dd)
U3 = CDVineSim(N,fam3,par3,nu3,type=2)

# calculate the log-likelihood
logLik3 = CDVineLogLik(U3,fam3,par3,nu3,type=2)
logLik3$loglik

Example output

The CDVine package is no longer developed actively.
Please consider using the more general VineCopula package
(see https://CRAN.R-project.org/package=VineCopula),
which extends and improves the functionality of CDVine.

          [,1]      [,2]      [,3]      [,4]      [,5]      [,6]
[1,] 0.7490381 0.5025327 0.8972044 0.6678854 0.0520954 0.5811345
[2,] 0.4937799 0.6758178 0.2057871 0.5833563 0.4718454 0.5754951
[3,] 0.8044155 0.6159832 0.5593707 0.2230294 0.4178685 0.6371460
[4,] 0.9253415 0.6181450 0.9252667 0.6979165 0.5013322 0.3194054
[5,] 0.3872614 0.8444397 0.6260970 0.1148200 0.4906736 0.7283768
[6,] 0.5684273 0.9629564 0.6423097 0.8668104 0.6613836 0.9110237
[1] 180.851
[1] 181.8809
[1] 144.2656

CDVine documentation built on May 2, 2019, 9:28 a.m.