Stochastic finite differences and multilevel Monte Carlo for a class of SPDEs in finance

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 orde...

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Main Authors: Giles, M, Reisinger, C
Format: Journal article
Published: SIAM 2012
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author Giles, M
Reisinger, C
author_facet Giles, M
Reisinger, C
author_sort Giles, M
collection OXFORD
description 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.
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spelling oxford-uuid:53cae5da-2a37-4e4a-ac87-bf392e83f0ed2022-03-26T16:33:55ZStochastic finite differences and multilevel Monte Carlo for a class of SPDEs in financeJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:53cae5da-2a37-4e4a-ac87-bf392e83f0edMathematical Institute - ePrintsSIAM2012Giles, MReisinger, CIn 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.
spellingShingle Giles, M
Reisinger, C
Stochastic finite differences and multilevel Monte Carlo for a class of SPDEs in finance
title Stochastic finite differences and multilevel Monte Carlo for a class of SPDEs in finance
title_full Stochastic finite differences and multilevel Monte Carlo for a class of SPDEs in finance
title_fullStr Stochastic finite differences and multilevel Monte Carlo for a class of SPDEs in finance
title_full_unstemmed Stochastic finite differences and multilevel Monte Carlo for a class of SPDEs in finance
title_short Stochastic finite differences and multilevel Monte Carlo for a class of SPDEs in finance
title_sort stochastic finite differences and multilevel monte carlo for a class of spdes in finance
work_keys_str_mv AT gilesm stochasticfinitedifferencesandmultilevelmontecarloforaclassofspdesinfinance
AT reisingerc stochasticfinitedifferencesandmultilevelmontecarloforaclassofspdesinfinance