Nonparametric inference on Lévy measures and copulas

In this paper nonparametric methods to assess the multivariate Lévy measure are introduced. Starting from high-frequency observations of a Lévy process $\mathbf{X}$, we construct estimators for its tail integrals and the Pareto-Lévy copula and prove weak convergence of these estimators in certain function spaces. Given n observations of increments over intervals of length $Δ_n$, the rate of convergence is $k_n^{-1/2}$ for $k_n=nΔ_n$ which is natural concerning inference on the Lévy measure. Besides extensions to nonequidistant sampling schemes analytic properties of the Pareto-Lévy 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.

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