A Variance Reduction Method for Parametrized Stochastic Differential Equations using the Reduced Basis Paradigm
Abstract
In this work, we develop a reduced-basis approach for the efficient computation of parametrized expected values, for a large number of parameter values, using the control variate method to reduce the variance. Two algorithms are proposed to compute online, through a cheap reduced-basis approximation, the control variates for the computation of a large number of expectations of a functional of a parametrized Ito stochastic process (solution to a parametrized stochastic differential equation). For each algorithm, a reduced basis of control variates is pre-computed offline, following a so-called greedy procedure, which minimizes the variance among a trial sample of the output parametrized expectations. Numerical results in situations relevant to practical applications (calibration of volatility in option pricing, and parameter-driven evolution of a vector field following a Langevin equation from kinetic theory) illustrate the efficiency of the method.
Cite
@article{arxiv.0906.3600,
title = {A Variance Reduction Method for Parametrized Stochastic Differential Equations using the Reduced Basis Paradigm},
author = {Sebastien Boyaval and Tony Lelievre},
journal= {arXiv preprint arXiv:0906.3600},
year = {2009}
}