An efficient Monte Carlo scheme for Zakai equations

Abstract

In this paper we develop a numerical method for efficiently approximating solutions of certain Zakai equations in high dimensions. The key idea is to transform a given Zakai SPDE into a PDE with random coefficients. We show that under suitable regularity assumptions on the coefficients of the Zakai equation, the corresponding random PDE admits a solution random field which, for almost all realizations of the random coefficients, can be written as a classical solution of a linear parabolic PDE. This makes it possible to apply the Feynman--Kac formula to obtain an efficient Monte Carlo scheme for computing approximate solutions of Zakai equations. The approach achieves good results in up to 25 dimensions with fast run times.

References in corpus (4)

• A proof that artificial neural networks overcome the curse of dimensionality in the numerical approximation of Black-Scholes partial differential equations
• Generalized backward doubly stochastic differential equations and SPDEs with nonlinear Neumann boundary conditions
• Deep learning based numerical approximation algorithms for stochastic partial differential equations
• On the strong regularity of degenerate additive noise driven stochastic differential equations with respect to their initial values