  There is an increasing interest to understand the dependence structure of a
random vector not only in the center of its distribution but also in the tails.
Extreme-value theory tackles the problem of modelling the joint tail of a
multivariate distribution by modelling the marginal distributions and the
dependence structure separately. For estimating dependence at high levels, the
stable tail dependence function and the spectral measure are particularly
convenient. These objects also lie at the basis of nonparametric techniques for
modelling the dependence among extremes in the max-domain of attraction
setting. In case of asymptotic independence, this setting is inadequate, and
more refined tail dependence coefficients exist, serving, among others, to
discriminate between asymptotic dependence and independence. Throughout, the
methods are illustrated on financial data.
