Nonparametric adaptive estimation of linear functionals for low frequency observed Lévy processes

For a Lévy process X having finite variation on compact sets and finite first moments, u (dx) = xv (dx) is a finite signed measure which completely describes the jump dynamics. We construct kernel estimators for linear functionals of u and provide rates of convergence under regularity assumptions. Moreover, we consider adaptive estimation via model selection and propose a new strategy for the data driven choice of the smoothing parameter.

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Year of publication:	2012
Authors:	Kappus, Johanna
Publisher:	Berlin : Humboldt University of Berlin, Collaborative Research Center 649 - Economic Risk
Subject:	Stochastischer Prozess \| Nichtparametrisches Verfahren \| Theorie \| statistics of stochastic processes \| low frequency observed Lévy processes \| nonparametric statistics \| adaptive estimation \| model selection with unknown variance

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Series:	SFB 649 Discussion Paper ; 2012-016
Type of publication:	Book / Working Paper
Type of publication (narrower categories):	Working Paper
Language:	English
Other identifiers:	68553586X [GVK] hdl:10419/56711 [Handle] RePEc:zbw:sfb649:sfb649dp2012-016 [RePEc]
Classification:	C14 - Semiparametric and Nonparametric Methods
Source:	EconStor

Persistent link: https://www.econbiz.de/10010281557