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Erschienen in: Journal of Scientific Computing 2/2016

27.04.2016

Multistep Schemes for Forward Backward Stochastic Differential Equations with Jumps

verfasst von: Yu Fu, Weidong Zhao, Tao Zhou

Erschienen in: Journal of Scientific Computing | Ausgabe 2/2016

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Abstract

In this work, we are concerned with multistep schemes for solving forward backward stochastic differential equations with jumps. The proposed multistep schemes admit many advantages. First of all, motivated by the local property of jump diffusion processes, the Euler method is used to solve the associated forward stochastic differential equation with jump, which reduce dramatically the entire computational complexity, however, the quantities of interests in the backward stochastic differential equations (with jump) are still of high order rate of convergence. Secondly, in each time step, only one jump is involved in the computational procedure, which again reduces dramatically the computational complexity. Finally, the method applies easily to partial-integral differential equations (and some nonlocal PDE models), by using the generalized Feynman–Kac formula. Several numerical experiments are presented to demonstrate the effectiveness of the proposed multistep schemes.

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Metadaten
Titel
Multistep Schemes for Forward Backward Stochastic Differential Equations with Jumps
verfasst von
Yu Fu
Weidong Zhao
Tao Zhou
Publikationsdatum
27.04.2016
Verlag
Springer US
Erschienen in
Journal of Scientific Computing / Ausgabe 2/2016
Print ISSN: 0885-7474
Elektronische ISSN: 1573-7691
DOI
https://doi.org/10.1007/s10915-016-0212-y

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