Backward Deep BSDE Methods and Applications to Nonlinear Problems
Author:
Affiliation:
1. Corporate Model Risk, Wells Fargo, New York, NY 10017, USA
Abstract
Publisher
MDPI AG
Subject
Strategy and Management,Economics, Econometrics and Finance (miscellaneous),Accounting
Link
https://www.mdpi.com/2227-9091/11/3/61/pdf
Reference12 articles.
1. Numerical methods for controlled Hamilton-Jacobi-Bellman PDEs in finance;Forsyth;Journal of Computational Finance,2007
2. Ganesan, Narayan, Yu, Yajie, and Hientzsch, Bernhard (2022). Pricing barrier options with deep backward stochastic differential equation methods. Journal of Computational Finance, 25, Available online: https://ssrn.com/abstract=3607626.
3. Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic differential equations;Han;Communications in Mathematics and Statistics,2017
4. Solving high-dimensional partial differential equations using deep learning;Han;Proceedings of the National Academy of Sciences,2018
5. Hientzsch, Bernhard (2023, March 15). Deep learning to solve forward-backward stochastic differential equations. Available online: https://ssrn.com/abstract=3494359.
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1. Numerical methods for backward stochastic differential equations: A survey;Probability Surveys;2023-01-01
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