Deep neural network framework based on backward stochastic differential equations for pricing and hedging American options in high dimensions-Reference-Cited by-同舟云学术

Deep neural network framework based on backward stochastic differential equations for pricing and hedging American options in high dimensions

Published:2020-09-08 Issue:1 Volume:21 Page:45-67
ISSN:1469-7688
Container-title:Quantitative Finance
language:en
Short-container-title:Quantitative Finance

Author:

Chen Yangang¹^ORCID,Wan Justin W. L.²^ORCID

Affiliation:

1. Department of Applied Mathematics, University of Waterloo, 200 University Avenue West, Waterloo, ON, N2L 3G1, Canada

2. David R. Cheriton School of Computer Science, University of Waterloo, 200 University Avenue West, Waterloo, ON, N2L 3G1, Canada

Funder

Natural Sciences and Engineering Research Council of Canada

Publisher

Informa UK Limited

Subject

General Economics, Econometrics and Finance,Finance

Link

https://www.tandfonline.com/doi/pdf/10.1080/14697688.2020.1788219

Reference37 articles.

1. null

2. Computational Methods for Option Pricing

3. Machine Learning Approximation Algorithms for High-Dimensional Fully Nonlinear Partial Differential Equations and Second-order Backward Stochastic Differential Equations

4. Becker, S., Cheridito, P., Jentzen, A. and Welti, T., Solving high-dimensional optimal stopping problems using deep learning. Preprint, 2019b. arXiv:1908.01602.

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