Hi all!

I have monthly returns from 30 instruments and most of them have asymmetric
distibutions and non-linear dependence structure. Thus I would like to fit a
copula-function into data to catch the non-linear dependence structure (and
eventually use scenario optimization as in e.g Scherer & Martin's (2005)
book "Modern Portfolio Optimization with NuOPT, S-Plus, and S-Bayes)

I would like to first fit the different copulas (normal, t, clayton,
gumbel) to data and then estimate which of them provide the best fit.

However, I am a bit overwhelmed by the subject because there are lots of
different methods to e.g measure the goodness-of-fit for copulas. So i would
like to ask opinions about how to best do the procedure..?? Could I just use
QRMlib packages fit.functions to fit the copulas using maximum likelihood
method? What is the preferable goodness-of-fit test? Does R provide an
Anderson-Darling test to measure the distance between fitted and empirical
copula?? I have been searching from packages, copula, fCopulae and QRMlib,
but haven't been able figure out how to proceed.

br,
John

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