Variance reduction methods for simulation of densities on Wiener space


Autoria(s): Kohatsu, Arturo; Pettersson, Roger
Contribuinte(s)

Universitat Pompeu Fabra. Departament d'Economia i Empresa

Data(s)

15/09/2005

Resumo

We develop a general error analysis framework for the Monte Carlo simulationof densities for functionals in Wiener space. We also study variancereduction methods with the help of Malliavin derivatives. For this, wegive some general heuristic principles which are applied to diffusionprocesses. A comparison with kernel density estimates is made.

Identificador

http://hdl.handle.net/10230/946

Idioma(s)

eng

Direitos

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info:eu-repo/semantics/openAccess

<a href="http://creativecommons.org/licenses/by-nc-nd/3.0/es/">http://creativecommons.org/licenses/by-nc-nd/3.0/es/</a>

Palavras-Chave #Statistics, Econometrics and Quantitative Methods #stochastic differential equations #weak approximation #variance reduction #kernel density estimation
Tipo

info:eu-repo/semantics/workingPaper