Approximation in law of locally $\alpha$-stable Lévy-type processes by non-linear regressions

Abstract

We study a real-valued Lévy-type process $X$, which is locally $\alpha$-stable in the sense that its jump kernel is a combination of a `principal' (state dependent) $\alpha$-stable part with a `residual' lower order part. We show that under mild conditions on the local characteristics of a process (the jump kernel and the velocity field) the process is uniquely defined, is Markov, and has the strong Feller property. We approximate $X$ in law by a non-linear regression ${\tilde{X}}_t^x={\mathfrak{f}}_t(x)+t^{1/\alpha }{U}_t^x$ with a deterministic regressor term ${\mathfrak{f}}_t(x)$ and $\alpha$-stable innovation term $U_t^x$, and provide error estimates for such an approximation. A case study is performed, revealing different types of assumptions which lead to various choices of regressor/innovation terms and various types of the estimates. The assumptions are quite general, cover the super-critical case $\alpha <1$, and allow non-symmetry of the Lévy kernel and unboundedness of the drift coefficient.

Minor revision after refeereeing. Accepted to Electronic Journal of Probability