  This paper deals with the problem of the multivariate copula density
estimation. Using wavelet methods we provide two shrinkage procedures based on
thresholding rules for which the knowledge of the regularity of the copula
density to be estimated is not necessary. These methods, said to be adaptive,
are proved to perform very well when adopting the minimax and the maxiset
approaches. Moreover we show that these procedures can be discriminated in the
maxiset sense. We produce an estimation algorithm whose qualities are evaluated
thanks some simulation. Last, we propose a real life application for financial
data.
