Finite Variance Unbiased Estimation of Stochastic Differential Equations

Agarwal, A. and Gobet, E. (2018) Finite Variance Unbiased Estimation of Stochastic Differential Equations. In: 2017 Winter Simulation Conference, Las Vegas, NV, USA, 03-06 Dec 2017, pp. 1950-1961. ISBN 9781538634288 (doi:10.1109/WSC.2017.8247930)

[img]
Preview
Text
147514.pdf - Accepted Version

346kB

Abstract

We develop a new unbiased estimation method for Lipschitz continuous functions of multi-dimensional stochastic differential equations with Lipschitz continuous coefficients. This method provides a finite variance estimator based on a probabilistic representation which is similar to the recent representations obtained through the parametrix method and recursive application of the automatic differentiation formula. Our approach relies on appropriate change of variables to carefully handle the singular integrands appearing in the iterated integrals of the probabilistic representation. It results in a scheme with randomized intermediate times where the number of intermediate times has a Pareto distribution

Item Type:Conference Proceedings
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Agarwal, Dr Ankush
Authors: Agarwal, A., and Gobet, E.
Subjects:Q Science > QA Mathematics
College/School:College of Social Sciences > Adam Smith Business School > Economics
ISSN:15584305
ISBN:9781538634288
Published Online:08 January 2018
Copyright Holders:Copyright © 2017 IEEE
First Published:First published in 2017 Winter Simulation Conference: 1950-1961
Publisher Policy:Reproduced in accordance with the publisher copyright policy
Related URLs:

University Staff: Request a correction | Enlighten Editors: Update this record