Nonparametric adaptive estimation for pure jump Lévy processes

Abstract

This paper is concerned with nonparametric estimation of the Lévy density of a pure jump Lévy process. The sample path is observed at $n$ discrete instants with fixed sampling interval. We construct a collection of estimators obtained by deconvolution methods and deduced from appropriate estimators of the characteristic function and its first derivative. We obtain a bound for the ${\mathbb{L}}^2$-risk, under general assumptions on the model. Then we propose a penalty function that allows to build an adaptive estimator. The risk bound for the adaptive estimator is obtained under additional assumptions on the Lévy density. Examples of models fitting in our framework are described and rates of convergence of the estimator are discussed.

References in corpus (4)

• Concentration around the mean for maxima of empirical processes
• Bilateral Gamma distributions and processes in financial mathematics
• Risk bounds for the non-parametric estimation of Lévy processes
• Nonparametric estimation for Lévy processes from low-frequency observations

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