  Likelihood-based procedures are a common way to estimate tail dependence
parameters. They are not applicable, however, in non-differentiable models such
as those arising from recent max-linear structural equation models. Moreover,
they can be hard to compute in higher dimensions. An adaptive weighted
least-squares procedure matching nonparametric estimates of the stable tail
dependence function with the corresponding values of a parametrically specified
proposal yields a novel minimum-distance estimator. The estimator is easy to
calculate and applies to a wide range of sampling schemes and tail dependence
models. In large samples, it is asymptotically normal with an explicit and
estimable covariance matrix. The minimum distance obtained forms the basis of a
goodness-of-fit statistic whose asymptotic distribution is chi-square.
Extensive Monte Carlo simulations confirm the excellent finite-sample
performance of the estimator and demonstrate that it is a strong competitor to
currently available methods. The estimator is then applied to disentangle
sources of tail dependence in European stock markets.
