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IMA Journal of Numerical Analysis Advance Access first published online on February 27, 2009
This version published online on November 6, 2009
IMA Journal of Numerical Analysis, doi:10.1093/imanum/drn067
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© The author 2009. Published by Oxford University Press on behalf of the Institute of Mathematics and its Applications. All rights reserved.

Weak convergence in the Prokhorov metric of methods for stochastic differential equations

Benoit Charbonneau

Mathematics Department, Duke University, Durham, NC, USA

Yuriy Svyrydov

Fakultät für Mathematik, Technische Universität München, Munich, Germany

P. F. Tupper{dagger}

Department of Mathematics, Simon Fraser University, Burnaby, BC, Canada

{dagger} Corresponding author. Email: tupper{at}math.mcgill.ca

Received on 30 July 2007. Revised on 31 January 2008.


  Abstract

We consider the weak convergence of numerical methods for stochastic differential equations (SDEs). Weak convergence is usually expressed in terms of the convergence of expected values of test functions of the trajectories. Here we present an alternative formulation of weak convergence in terms of the well-known Prokhorov metric on spaces of random variables. For a general class of methods we establish bounds on the rates of convergence in terms of the Prokhorov metric. In doing so, we revisit the original proofs of weak convergence and show explicitly how the bounds on the error depend on the smoothness of the test functions. As an application of our result, we use the Strassen–Dudley theorem to show that the numerical approximation and the true solution to the system of SDEs can be re-embedded in a probability space in such a way that the method converges there in a strong sense. One corollary of this last result is that the method converges in the Wasserstein distance, another metric on spaces of random variables. Another corollary establishes rates of convergence for expected values of test functions, assuming only local Lipschitz continuity. We conclude with a review of the existing results for pathwise convergence of weakly converging methods and the corresponding strong results available under re-embedding.

Key Words: stochastic differential equations; numerical methods; convergence in distribution; weak convergence; Prokhorov metric; Strassen–Dudley theorem; Wasserstein distance

The original version was incorrect due to two citations in the article have now been updated.


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