Invariant density adaptive estimation for ergodic jump–diffusion processes over anisotropic classes

Abstract

We consider the solution X=(Xt)t≥0 of a multivariate stochastic differential equation with Levy-type jumps and with unique invariant probability measure with density μ. We assume that a continuous record of observations XT=(Xt)0≤t≤T is available.In the case without jumps, Dalalyan and Reiss (2007) and Strauch (2018) have found convergence rates of invariant density estimators, under respectively isotropic and anisotropic Hölder smoothness constraints, which are considerably faster than those known from standard multivariate density estimation.We extend the previous works by obtaining, in presence of jumps, some estimators which have the same convergence rates they had in the case without jumps for d≥2 and a rate which depends on the degree of the jumps in the one-dimensional setting. We propose moreover a data driven bandwidth selection procedure based on the Goldenshluger and Lepski method (Goldenshluger and Lepski, 2011) which leads us to an adaptive non-parametric kernel estimator of the stationary density μ of the jump–diffusion X.