  Consider a random sample in the max-domain of attraction of a multivariate
extreme value distribution such that the dependence structure of the attractor
belongs to a parametric model. A new estimator for the unknown parameter is
defined as the value that minimizes the distance between a vector of weighted
integrals of the tail dependence function and their empirical counterparts. The
minimization problem has, with probability tending to one, a unique, global
solution. The estimator is consistent and asymptotically normal. The spectral
measures of the tail dependence models to which the method applies can be
discrete or continuous. Examples demonstrate the applicability and the
performance of the method.
