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)

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

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