Skip to Main content Skip to Navigation

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 X T = (Xt) 0≤t≤T is available. In the case without jumps, Reiss and Dalalyan (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 Goldensh-luger and Lepski (2011) method which leads us to an adaptive non-parametric kernel estimator of the stationary density µ of the jump diffusion X. Adaptive bandwidth selection, anisotropic density estimation, ergodic diffusion with jumps, Lévy driven SDE
Document type :
Preprints, Working Papers, ...
Complete list of metadatas

Cited literature [26 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-02442374
Contributor : Chiara Amorino <>
Submitted on : Thursday, January 16, 2020 - 1:44:11 PM
Last modification on : Thursday, March 19, 2020 - 12:26:03 PM

Files

main-3.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-02442374, version 1
  • ARXIV : 2001.07422

Citation

Chiara Amorino, Arnaud Gloter. Invariant density adaptive estimation for ergodic jump diffusion processes over anisotropic classes. 2020. ⟨hal-02442374⟩

Share

Metrics

Record views

69

Files downloads

35