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Stochastic finite differences and multilevel Monte Carlo for a class of SPDEs in finance. (English) Zbl 1257.35200

Summary: In this article, we propose a Milstein finite difference scheme for a stochastic partial differential equation (SPDE) describing a large particle system. We show, by means of Fourier analysis, that the discretization on an unbounded domain is convergent of first order in the timestep and second order in the spatial grid size, and that the discretization is stable with respect to boundary data. Numerical experiments clearly indicate that the same convergence order also holds for boundary value problems. Multilevel path simulation, previously used for SDEs, is shown to give substantial complexity gains compared to a standard discretization of the SPDE or direct simulation of the particle system. We derive complexity bounds and illustrate the results by an application to basket credit derivatives.

MSC:

35R60 PDEs with randomness, stochastic partial differential equations
35Q91 PDEs in connection with game theory, economics, social and behavioral sciences
60H15 Stochastic partial differential equations (aspects of stochastic analysis)
65C05 Monte Carlo methods
65N06 Finite difference methods for boundary value problems involving PDEs
65N12 Stability and convergence of numerical methods for boundary value problems involving PDEs
91G40 Credit risk
91G60 Numerical methods (including Monte Carlo methods)