Robust and Stable Deep Learning Algorithms for Forward Backward Stochastic Differential Equations

Timestamps
0:00 - Intro
2:39 - Partial Differential Equations
3:43 - Stochastic Differential Equations
6:08 - Brownian Motion
8:06 - Non-linear PDEs
8:52 - Designing a Neural Network
12:28 - Black-Scholes example
13:47 - Stability and Generalisation
15:45 - Computational Efficiency
18:25 - Conclusion
20:08 - Q&A

Data Science Meetup of the Analytics Club at ETH Zurich

Speaker: Alexis Laignelet (PhD Student at Imperial College London)
Publication: https://arxiv.org/abs/1910.11623
Presentation and more: https://github.com/alaignelet/alaignelet.github.io/tree/master/pdfs
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Paper Abstract:

Applications in quantitative finance such as optimal trade execution, risk management of options, and optimal asset allocation involve the solution of high dimensional and nonlinear Partial Differential Equations (PDEs). The connection between PDEs and systems of Forward-Backward Stochastic Differential Equations (FBSDEs) enables the use of advanced simulation techniques to be applied even in the high dimensional setting. Unfortunately, when the underlying application contains nonlinear terms, then classical methods both for simulation and numerical methods for PDEs suffer from the curse of dimensionality. Inspired by the success of deep learning, several researchers have recently proposed to address the solution of FBSDEs using deep learning. We discuss the dynamical systems point of view of deep learning and compare several architectures in terms of stability, generalization, and robustness. In order to speed up the computations, we propose to use a multilevel discretization technique. Our preliminary results suggest that the multilevel discretization method improves solutions times by an order of magnitude compared to existing methods without sacrificing stability or robustness.

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