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The optimal stopping problem is one of the core problems in financial markets, with broad applications such as pricing American and Bermudan options.
Valuing american options by simulation: a simple least-squares approach
Francis A Longstaff and Eduardo S Schwartz · 2001
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Discrete-time approximation and monte-carlo simulation of backward stochastic differential equations
Bruno Bouchard and Nizar Touzi · 2004
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A numerical scheme for BSDEs
Jianfeng Zhang · 2004
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A quantization tree method for pricing and hedging multidimensional american options
Vlad Bally, Gilles Pagès, and Jacques Printems · 2005
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A regression-based monte carlo method to solve backward stochastic differential equations
Emmanuel Gobet, Jean-Philippe Lemor, and Xavier Warin · 2005
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A forward scheme for backward sdes
Christian Bender and Robert Denk · 2007
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Time discretization and Markovian iteration for coupled FBSDEs
Christian Bender and Jianfeng Zhang · 2008
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Learning exercise policies for american options
Yuxi Li, Csaba Szepesvari, and Dale Schuurmans · 2009
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Pricing of high-dimensional american options by neural networks
Michael Kohler, Adam Krzyżak, and Nebojsa Todorovic · 2010
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A stable multistep scheme for solving backward stochastic differential equations
Weidong Zhao, Guannan Zhang, and Lili Ju · 2010
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Solving backward stochastic differential equations using the cubature method: application to nonlinear pricing
Dan Crisan and Konstantinos Manolarakis · 2012
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A monotone scheme for high-dimensional fully nonlinear pdes
Wenjie Guo, Jianfeng Zhang, and Jia Zhuo · 2015
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Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic differential equations
Jiequn Han, Arnulf Jentzen, et al · 2017
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Backward stochastic differential equations: From Linear to Fully Nonlinear Theory
Jianfeng Zhang · 2017
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Solving high-dimensional partial differential equations using deep learning
Jiequn Han, Arnulf Jentzen, and E Weinan · 2018
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Haojie Wang, Han Chen, Agus Sudjianto, Richard Liu, and Qi Shen · 2018
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Deep learning for ranking response surfaces with applications to optimal stopping problems
Ruimeng Hu · 2020
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Deep backward schemes for high-dimensional nonlinear pdes
Côme Huré, Huyên Pham, and Xavier Warin · 2020
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Optimal stopping via randomized neural networks
Calypso Herrera, Florian Krach, Pierre Ruyssen, and Josef Teichmann · 2021
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Deep fictitious play for stochastic differential games
Ruimeng Hu · 2021
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Neural network regression for bermudan option pricing
Bernard Lapeyre and Jérôme Lelong · 2021
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Convergence of a robust deep fbsde method for stochastic control
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Deep optimal stopping
Sebastian Becker, Patrick Cheridito, and Arnulf Jentzen · 2019
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René Carmona and Mathieu Laurière · 2019
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Pricing and hedging american-style options with deep learning
Sebastian Becker, Patrick Cheridito, and Arnulf Jentzen · 2020
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Convergence of deep fictitious play for stochastic differential games
Jiequn Han, Ruimeng Hu, and Jihao Long · 2020
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Convergence of the deep BSDE method for coupled FBSDEs
Jiequn Han and Jihao Long · 2020
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Kristoffer Andersson, Adam Andersson, and Cornelis W Oosterlee · 2022
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Deep signature algorithm for path-dependent american option pricing
Erhan Bayraktar, Qi Feng, and Zhaoyu Zhang · 2022
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Recent developments in machine learning methods for stochastic control and games
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