VineCopula (version 1.0)

C2RVine: Transform C-vine to R-vine structure

Description

This function transforms a C-vine structure from the package CDVine to the corresponding R-vine structure.

Usage

C2RVine(order, family, par, par2=rep(0,length(family)))

Arguments

order

A d-dimensional vector specifying the order of the root nodes in the C-vine.

family

A d*(d-1)/2 vector of pair-copula families with values 0 = independence copula 1 = Gaussian copula 2 = Student t copula (t-copula) 3 = Clayton copula 4 = Gumbel copula 5 =

par

A d*(d-1)/2 vector of pair-copula parameters.

par2

A d*(d-1)/2 vector of second pair-copula parameters (optional; default: par2 = rep(0,length(family))), necessary for the t-, BB1, BB6, BB7 and BB8 copulas.

Value

• An RVineMatrix object.

References

Dissmann, J. F., E. C. Brechmann, C. Czado, and D. Kurowicka (2011). Selecting and estimating regular vine copulae and application to financial returns. Submitted for publication. http://mediatum.ub.tum.de/node?id=1079277 Kurowicka, D. and H. Joe (Eds.) (2011). DEPENDENCE MODELING: Vine Copula Handbook. Singapore: World Scientific Publishing Co.

See Also

RVineMatrix, D2RVine, R2CVine

Examples

# simulate a sample of size 500 from a 4-dimensional C-vine  
# copula model with mixed pair-copulas
# load package CDVine
library(CDVine)
d = 4
dd = d*(d-1)/2
order = 1:d
family = c(1,2,3,4,7,3)
par = c(0.5,0.4,2,1.5,1.2,1.5)
par2 = c(0,5,0,0,2,0)
type = 1
simdata = CDVineSim(500,family,par,par2,type)

# determine log-likelihood
out = CDVineLogLik(simdata,family,par,par2,type)
out$loglik

# transform to R-vine matrix notation
RVM = C2RVine(order,family,par,par2)

# check that log-likelihood stays the same
out2 = RVineLogLik(simdata,RVM)
out2$loglik