Deep learning to solve forward-backward stochastic differential equations

Pricing vanilla and exotic options with a deep learning approach for PDEs


Bernhard Hientzsch describes how final-value problems can be turned into control problems, which can be time discretised and time stepped, to obtain both forward and backward time-stepped, time-discrete stochastic control problems. Representing the controls as deep neural networks, these optimisation problems can be solved using deep learning techniques

In this article, first, we discuss the general risk factor dynamics treated and prototypical instruments and

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