Deep stochastic optimization in finance-Reference-Cited by-同舟云学术

Deep stochastic optimization in finance

Published:2022-12-23 Issue:1 Volume:5 Page:91-111
ISSN:2524-6984
Container-title:Digital Finance
language:en
Short-container-title:Digit Finance

Author:

Reppen A. Max,Soner H. Mete^ORCID,Tissot-Daguette Valentin

Funder

National Science Foundation

Publisher

Springer Science and Business Media LLC

Subject

General Engineering

Link

https://link.springer.com/content/pdf/10.1007/s42521-022-00074-6.pdf

Reference40 articles.

1. Andersen, L., & Broadie, M. (2004). Primal-dual simulation algorithm for pricing multidimensional American options. Management Science, 50(9), 1222–1234.

2. Bachouch, A., Huré, C., Langrené, N., & Pham, H. (2018). Deep neural networks algorithms for stochastic control problems on finite horizon, part 2: Numerical applications. arXiv:1812.05916.

3. Bayer, C., Tempone, R., & Wolfers, S. (2020). Pricing American options by exercise rate optimization. Quantitative Finance, 20(11), 1749–1760.

4. Becker, S., Cheridito, P., & Jentzen, A. (2019). Deep optimal stopping. Journal of Machine Learning Research, 20(4), 1–25.

5. Becker, S., Cheridito, P., Jentzen, A., & Welti, T. (2021). Solving high-dimensional optimal stopping problems using deep learning. European Journal of Applied Mathematics, 32(3), 470–514.

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