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An alternative to describe tail dependence can be found in the Ledford &#38; Tawn (1996) for instance. The intuition behind can be found in Fischer &#38; 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), thenSo assume that there is a  such thatThen =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 towhich will be interpreted as a (weak) tail dependence index. Thus define concentration functionsfor the lower tail (on the left) andfor the upper tail (on the right). The R code to compute those functions is quite simple,&#62; library(evd); &#62; data(lossalae)&#62; X=lossalae&#62; U=rank(X[,1])/(nrow(X)+1)&#62; V=rank(X[,2])/(nrow(X)+1&#62; fL2emp=function(z) 2*log(mean(U&#60;=z))/+ log(mean((U&#60;=z)&#38;(V&#60;=z)))-1&#62; fR2emp=function(z) 2*log(mean(U&#62;=1-z))/+ log(mean((U&#62;=1-z)&#38;(V&#62;=1-z)))-1&#62; u=seq(.001,.5,by=.001)&#62; L=Vectorize(fL2emp)(u)&#62; R=Vectorize(fR2emp)(rev(u))&#62; plot(c(u,u+.5-u[1]),c(L,R),type="l",ylim=0:1,+ xlab="LOWER TAIL      UPPER TAIL")&#62; 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)&#62; tau=cor(lossalae,method="kendall")[1,2]&#62; library(copula)&#62; paramgauss=sin(tau*pi/2)&#62; copgauss=normalCopula(paramgauss)&#62; Lgaussian=function(z) 2*log(z)/log(pCopula(c(z,z),+ copgauss))-1&#62; Rgaussian=function(z) 2*log(1-z)/log(1-2*z++ pCopula(c(z,z),copgauss))-1&#62; u=seq(.001,.5,by=.001)&#62; Lgs=Vectorize(Lgaussian)(u)&#62; Rgs=Vectorize(Rgaussian)(1-rev(u))&#62; lines(c(u,u+.5-u[1]),c(Lgs,Rgs),col="red")or Gumbel copula,&#62; paramgumbel=1/(1-tau)&#62; copgumbel=gumbelCopula(paramgumbel, dim = 2)&#62; Lgumbel=function(z) 2*log(z)/log(pCopula(c(z,z),+ copgumbel))-1&#62; Rgumbel=function(z) 2*log(1-z)/log(1-2*z++ pCopula(c(z,z),copgumbel))-1&#62; Lgl=Vectorize(Lgumbel)(u)&#62; Rgl=Vectorize(Rgumbel)(1-rev(u))&#62; 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.