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, µ( dx) = xv( dx) is a finite signed measure which completely describes the jump dynamics. We construct kernel estimators for linear functionals of µ 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:	2012-02
Authors:	Kappus, Johanna
Institutions:	Sonderforschungsbereich 649: Ökonomisches Risiko, Wirtschaftswissenschaftliche Fakultät
Subject:	Statistics of stochastic processes \| Low frequency observed Lévy processes \| Nonparametric statistics \| Adaptive estimation \| Model selection with unknown variance

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Extent:	application/pdf
Series:	SFB 649 Discussion Papers.
Type of publication:	Book / Working Paper
Language:	English
Notes:	Number SFB649DP2012-016 39 pages
Classification:	C14 - Semiparametric and Nonparametric Methods
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10009645831