In this paper, we propose an efficient Monte Carlo implementation of non-linear FBSDEs as a system of interacting particles by developing a variant of marked branching diffusion method. It will be particularly useful to investigate large and complex systems, and hence it is a good complement of our previous work presenting an analytical perturbation procedure for generic non-linear FBSDEs. There appear multiple species of particles, where the first one follows the diffusion of the original underlying state, and the others the Malliavin derivatives with a grading structure. In contrast to the naive implementation of marked branching diffusion, the number of branches as well as interactions are efficiently suppressed by the properties of the perturbative expansion. This property will make the numerical implementation more feasible and stable. Furthermore, there is no need of direct approximation of the non-linear driver by a polynomial function . The proposed method can be applied to semi-linear problems, such as American and Bermudan options, Credit Value Adjustment (CVA), and even fully non-linear issues, such as the optimal portfolio problems in incomplete and/or constrained markets, feedbacks from large investors, and also the analysis of various risk measures.
In this paper, we propose an efficient Monte Carlo implementation of non-linear FBSDEs as a system of interacting particles inspired by the ideas of branching diffusion method. It will be particularly useful to investigate large and complex systems, and hence it is a good complement of our previous work presenting an analytical perturbation procedure for generic non-linear FBSDEs. There appear multiple species of particles, where the first one follows the diffusion of the original underlying state, and the others the Malliavin derivatives with a grading structure. The number of branching points are capped by the order of perturbation, which is expected to make the scheme less numerically intensive . The proposed method can be applied to semi-linear problems, such as American and Bermudan options, Credit Value Adjustment (CVA), and even fully non-linear issues, such as the optimal portfolio problems in incomplete and/or constrained markets, feedbacks from large investors, and also the analysis of various risk measures.
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In contrast to the naive implementation of marked branching diffusion, the number of branches as well as interactions are efficiently suppressed by the properties of the perturbative expansion. This property will make the numerical implementation more feasible and stable. Furthermore, there is no need of direct approximation of the non-linear driver by a polynomial function
The number of branching points are capped by the order of perturbation, which is expected to make the scheme less numerically intensive