On the functional estimation of multivariate diffusion processes

Federico Bandi and Guillermo Moloche

MPRA Paper from University Library of Munich, Germany

Abstract: We propose a fully nonparametric estimation theory for the drift vector and the diffusion matrix of multivariate diffusion processes. The estimators are sample analogues to infinitesimal conditional expectations constructed as Nadaraya-Watson kernel averages. Minimal assumptions are imposed on the statistical properties of the multivariate system to obtain limiting results. Harris recurrence is all that we require to show strong consistency and asymptotic (mixed) normality of the functional estimates. Hence, the estimation method and asymptotic theory apply to both stationary and nonstationary multivariate diffusion processes of the recurrent type.

Keywords: Diffusion processes; nonparametric estimation (search for similar items in EconPapers)
JEL-codes: C01 C14 C32 (search for similar items in EconPapers)
Date: 2008-07-01
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (16)

Downloads: (external link)
https://mpra.ub.uni-muenchen.de/43681/1/MPRA_paper_43681.pdf original version (application/pdf)

Related works:
Journal Article: ON THE FUNCTIONAL ESTIMATION OF MULTIVARIATE DIFFUSION PROCESSES (2018)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:43681

Access Statistics for this paper

More papers in MPRA Paper from University Library of Munich, Germany Ludwigstraße 33, D-80539 Munich, Germany. Contact information at EDIRC.
Bibliographic data for series maintained by Joachim Winter ().