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Deep backward schemes for high-dimensional nonlinear PDEs
We analyze the convergence of the deep learning schemes and provide error estimates in terms of the universal approximation of neural networks.
Deep neural networks algorithms for stochastic control problems on finite horizon: numerical applications
This paper presents several numerical applications of deep learning-based algorithms that have been introduced in [HPBL18].
Deep neural networks algorithms for stochastic control problems on finite horizon: convergence analysis
This paper develops algorithms for high-dimensional stochastic control problems based on deep learning and dynamic programming.
Algorithmic trading in a microstructural limit order book model
In particular, we simulated an order book with constant/ symmet-ric/ asymmetrical/ state dependent intensities, and compared the computed optimal strategy with naive strategies.
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