Variance reduction methods for simulation of densities on Wiener space

We develop a general error analysis framework for the Monte Carlo simulation of densities for functionals in Wiener space. We also study variance reduction methods with the help of Malliavin derivatives. For this, we give some general heuristic principles which are applied to diffusion processes. A comparison with kernel density estimates is made.

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Year of publication:	2002-01
Authors:	Kohatsu, Arturo ; Pettersson, Roger
Institutions:	Department of Economics and Business, Universitat Pompeu Fabra
Subject:	Stochastic differential equations \| weak approximation \| variance reduction \| kernel density estimation

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Extent:	application/pdf
Series:	Economics Working Papers.
Type of publication:	Book / Working Paper
Language:	English
Classification:	G13 - Contingent Pricing; Futures Pricing
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10005772153