  Nonparametric methods for the estimation of the Levy density of a Levy
process are developed. Estimators that can be written in terms of the ``jumps''
of the process are introduced, and so are discrete-data based approximations. A
model selection approach made up of two steps is investigated. The first step
consists in the selection of a good estimator from a linear model of proposed
Levy densities, while the second is a data-driven selection of a linear model
among a given collection of linear models. By providing lower bounds for the
minimax risk of estimation over Besov Levy densities, our estimators are shown
to achieve the ``best'' rate of convergence. A numerical study for the case of
histogram estimators and for variance Gamma processes, models of key importance
in risky asset price modeling driven by Levy processes, is presented.
