| Issue |
ESAIM: PS
Volume 21, 2017
|
|
|---|---|---|
| Page(s) | 56 - 87 | |
| DOI | https://doi.org/10.1051/ps/2017001 | |
| Published online | 09 March 2017 | |
Unbiased Monte Carlo estimate of stochastic differential equations expectations
EDF R&D & FiME, Laboratoire de Finance des Marchés de l’Energie (www.fime-lab.org), 7 boulevard Gaspard Monge, 91120 Palaiseau, France.
xavier.warin@gmail.com
Received: 26 January 2016
Revised: 15 July 2016
Accepted: 18 January 2016
We propose an unbiased Monte Carlo method to compute E(g(XT)) where g is a Lipschitz function and X an Ito process. This approach extends the method proposed in [16] to the case where X is solution of a multidimensional stochastic differential equation with varying drift and diffusion coefficients. A variance reduction method relying on interacting particle systems is also developed.
Mathematics Subject Classification: 65C05 / 60J60 / 60J85 / 35K10
Key words: Unbiased estimate / linear parabolic PDEs / interacting particle systems
© EDP Sciences, SMAI, 2017
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