Nonparametric inference on L\'evy measures and copulas

In this paper nonparametric methods to assess the multivariate L'{e}vy measure are introduced. Starting from high-frequency observations of a L'{e}vy process $\mathbf{X}$, we construct estimators for its tail integrals and the Pareto-L'{e}vy copula and prove weak convergence of these estimators in certain function spaces. Given n observations of increments over intervals of length ${\mathrm{\Delta }}_n$, the rate of convergence is $k_n^{-1\mathrm{/}2}$ for $k_n=n{\mathrm{\Delta }}_n$ which is natural concerning inference on the L'{e}vy measure. Besides extensions to nonequidistant sampling schemes analytic properties of the Pareto-L'{e}vy copula which, to the best of our knowledge, have not been mentioned before in the literature are provided as well. We conclude with a short simulation study on the performance of our estimators and apply them to real data.