  Based on the concept of a L\'evy copula to describe the dependence structure
of a multivariate L\'evy process we present a new estimation procedure. We
consider a parametric model for the marginal L\'evy processes as well as for
the L\'evy copula and estimate the parameters by a two-step procedure. We first
estimate the parameters of the marginal processes, and then estimate in a
second step only the dependence structure parameter. For infinite L\'evy
measures we truncate the small jumps and base our statistical analysis on the
large jumps of the model. Prominent example will be a bivariate stable \lp,
which allows for analytic calculations and, hence, for a comparison of
different methods. We prove asymptotic normality of the parameter estimates
from the two-step procedure and, in particular, we derive the Godambe
information matrix, whose inverse is the covariance matrix of the normal limit
law. A simulation study investigates the loss of efficiency because of the
two-step procedure and the truncation.
