Discretization and Machine Learning Approximation of BSDEs with a Constraint on the Gains-Process

Idris Kharroubi, Thomas Lim and Xavier Warin
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Idris Kharroubi: LPSM UMR 8001
Thomas Lim: LaMME, ENSIIE
Xavier Warin: EDF

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Abstract: We study the approximation of backward stochastic differential equations (BSDEs for short) with a constraint on the gains process. We first discretize the constraint by applying a so-called facelift operator at times of a grid. We show that this discretely constrained BSDE converges to the continuously constrained one as the mesh grid converges to zero. We then focus on the approximation of the discretely constrained BSDE. For that we adopt a machine learning approach. We show that the facelift can be approximated by an optimization problem over a class of neural networks under constraints on the neural network and its derivative. We then derive an algorithm converging to the discretely constrained BSDE as the number of neurons goes to infinity. We end by numerical experiments. Mathematics Subject Classification (2010): 65C30, 65M75, 60H35, 93E20, 49L25.

Date: 2020-02
New Economics Papers: this item is included in nep-big and nep-cmp
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