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E, W.N., Han, J. and Jentzen, A. (2017) Deep Learning-Based Numerical Methods for High-Dimensional Parabolic Partial Deferential Equations and Backward Stochastic Differential Equations. Working Paper. https://arxiv.org/pdf/1706.04702.pdf

has been cited by the following article:

• TITLE: A Clustering Method to Solve Backward Stochastic Differential Equations with Jumps

  AUTHORS: Liangliang Zhang

  KEYWORDS: Backward Stochastic Differential Equation with Jumps, Jump Diffusion, Clus-tering, Weak Convergence

  JOURNAL NAME: Journal of Mathematical Finance, Vol.10 No.1, December 13, 2019

  ABSTRACT: In this paper, we introduce a clustering method to approximate the solution to a general Backward Stochastic Differential Equation with Jumps (BSDEJ). We show the convergence of the sequence of approximate solutions to the true one. The method is implemented for an application in finance. Numerical results show that the method is efficient.