# Copulas and tail dependence, part 2

September 18, 2012
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(This article was first published on Freakonometrics - Tag - R-english, and kindly contributed to R-bloggers)

An alternative to describe tail dependence can be found in the Ledford & Tawn (1996) for instance. The intuition behind can be found in Fischer & Klein (2007)). Assume that and have the same distribution. Now, if we assume that those variables are (strictly) independent,

But if we assume that those variables are (strictly) comonotonic (i.e. equal here since they have the same distribution), then

So assume that there is a
such that
Then =2 can be interpreted as independence while =1 means strong (perfect) positive dependence. Thus, consider the following transformation to get a parameter in [0,1], with a strength of dependence increasing with the index, e.g.

In order to derive a tail dependence index, assume that there exists a limit to

which will be interpreted as a (weak) tail dependence
index
. Thus define concentration functions

for the lower tail (on the left) and

for the upper tail (on the right). The R code to compute those functions is quite simple,

```> library(evd);
> data(lossalae)
> X=lossalae
> U=rank(X[,1])/(nrow(X)+1)
> V=rank(X[,2])/(nrow(X)+1
> fL2emp=function(z) 2*log(mean(U<=z))/
+ log(mean((U<=z)&(V<=z)))-1
> fR2emp=function(z) 2*log(mean(U>=1-z))/
+ log(mean((U>=1-z)&(V>=1-z)))-1
> u=seq(.001,.5,by=.001)
> L=Vectorize(fL2emp)(u)
> R=Vectorize(fR2emp)(rev(u))
> plot(c(u,u+.5-u[1]),c(L,R),type="l",ylim=0:1,
+ xlab="LOWER TAIL      UPPER TAIL")
> abline(v=.5,col="grey")```

and again, it is possible to plot those empirical functions against some parametric ones, e.g. the one obtained from a Gaussian copula (with the same Kendall’s tau)

```> tau=cor(lossalae,method="kendall")[1,2]
> library(copula)
> paramgauss=sin(tau*pi/2)
> copgauss=normalCopula(paramgauss)
> Lgaussian=function(z) 2*log(z)/log(pCopula(c(z,z),
+ copgauss))-1
> Rgaussian=function(z) 2*log(1-z)/log(1-2*z+
+ pCopula(c(z,z),copgauss))-1
> u=seq(.001,.5,by=.001)
> Lgs=Vectorize(Lgaussian)(u)
> Rgs=Vectorize(Rgaussian)(1-rev(u))
> lines(c(u,u+.5-u[1]),c(Lgs,Rgs),col="red")```

or Gumbel copula,

```> paramgumbel=1/(1-tau)
> copgumbel=gumbelCopula(paramgumbel, dim = 2)
> Lgumbel=function(z) 2*log(z)/log(pCopula(c(z,z),
+ copgumbel))-1
> Rgumbel=function(z) 2*log(1-z)/log(1-2*z+
+ pCopula(c(z,z),copgumbel))-1
> Lgl=Vectorize(Lgumbel)(u)
> Rgl=Vectorize(Rgumbel)(1-rev(u))
> lines(c(u,u+.5-u[1]),c(Lgl,Rgl),col="blue")```

Again, one should look more carefully at confidence bands, but is looks like Gumbel copula provides a good fit here.

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