Sequential Monte Carlo methods for diffusion processes

Ajay Jasra, Arnaud Doucet

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

In this paper, we show how to use sequential Monte Carlo methods to compute expectations of functionals of diffusions at a given time and the gradients of these quantities w.r.t. the initial condition of the process. In some cases, via the exact simulation of the diffusion, there is no time discretization error, otherwise the methods use Euler discretization. We illustrate our approach on both high-and low-dimensional problems from optimal control and establish that our approach substantially outperforms standard Monte Carlo methods typically adopted in the literature. The methods developed here are appropriate for solving a certain class of partial differential equations as well as for option pricing and hedging. © 2009 The Royal Society.
Original languageEnglish (US)
JournalProceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences
Volume465
Issue number2112
DOIs
StatePublished - Dec 8 2009
Externally publishedYes

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