Neural networks-based algorithms for stochastic control and PDEs in finance *

Maximilien Germain (), Huyên Pham () and Xavier Warin
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Maximilien Germain: LPSM (UMR_8001) - Laboratoire de Probabilités, Statistique et Modélisation - SU - Sorbonne Université - CNRS - Centre National de la Recherche Scientifique - UPCité - Université Paris Cité, EDF R&D - EDF R&D - EDF - EDF, EDF - EDF, EDF R&D OSIRIS - Optimisation, Simulation, Risque et Statistiques pour les Marchés de l’Energie - EDF R&D - EDF R&D - EDF - EDF
Huyên Pham: LPSM (UMR_8001) - Laboratoire de Probabilités, Statistique et Modélisation - UPD7 - Université Paris Diderot - Paris 7 - SU - Sorbonne Université - CNRS - Centre National de la Recherche Scientifique, FiME Lab - Laboratoire de Finance des Marchés d'Energie - Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres - CREST - EDF R&D - EDF R&D - EDF - EDF
Xavier Warin: EDF R&D - EDF R&D - EDF - EDF, FiME Lab - Laboratoire de Finance des Marchés d'Energie - Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres - CREST - EDF R&D - EDF R&D - EDF - EDF, EDF - EDF, EDF R&D OSIRIS - Optimisation, Simulation, Risque et Statistiques pour les Marchés de l’Energie - EDF R&D - EDF R&D - EDF - EDF

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Abstract: This paper presents machine learning techniques and deep reinforcement learningbased algorithms for the efficient resolution of nonlinear partial differential equations and dynamic optimization problems arising in investment decisions and derivative pricing in financial engineering. We survey recent results in the literature, present new developments, notably in the fully nonlinear case, and compare the different schemes illustrated by numerical tests on various financial applications. We conclude by highlighting some future research directions.

Date: 2021
New Economics Papers: this item is included in nep-big, nep-cmp and nep-ore
Note: View the original document on HAL open archive server: https://hal.science/hal-03115503v2
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Citations: View citations in EconPapers (14)

Published in A. Capponi. and C.A. Lehalle. Machine Learning And Data Sciences For Financial Markets: A Guide To Contemporary Practices, Cambridge University Press, pp.426-452, 2021, 9781009028943. ⟨10.1017/9781009028943.023⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-03115503

DOI: 10.1017/9781009028943.023

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