Math @ Duke

Publications [#156863] of Benoit Charbonneau
Papers Published
 Benoit Charbonneau, Yuriy Svyrydov, and P.F. Tupper, Convergence in the Prokhorov Metric of Weak Methods for Stochastic Differential Equations,
IMA Journal of Numerical Analysis, vol. 30 no. 2
(2010),
pp. 579594 [drn067], [doi]
(last updated on 2010/03/27)
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 wellknown 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 Strassenâ€“Dudley theorem to show that the numerical approximation and the true solution to the system of SDEs can be reembedded 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 reembedding.
Keywords: stochastic differential equations • numerical methods • convergence in distribution • weak convergence • Prokhorov metric • Strassenâ€“Dudley theorem • Wasserstein distance


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