Asymptotic equivalence for pure jump Lévy processes with unknown Lévy density and Gaussian white noise

Abstract

The aim of this paper is to establish a global asymptotic equivalence between the experiments generated by the discrete (high frequency) or continuous observation of a path of a L{é}vy process and a Gaussian white noise experiment observed up to a time T, with T tending to $\infty$. These approximations are given in the sense of the Le Cam distance, under some smoothness conditions on the unknown L{é}vy density. All the asymptotic equivalences are established by constructing explicit Markov kernels that can be used to reproduce one experiment from the other.

50 pages. The definition of the parameter space has changed and some proofs have been expanded and corrected