Deep empirical risk minimization in finance: Looking into the future-Reference-Cited by-同舟云学术

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Deep empirical risk minimization in finance: Looking into the future

Published:2022-11-17 Issue:1 Volume:33 Page:116-145
ISSN:0960-1627
Container-title:Mathematical Finance
language:en
Short-container-title:Mathematical Finance

Author:

Reppen Anders Max¹,Soner Halil Mete²^ORCID

Affiliation:

1. Questrom School of Business Boston University Boston Massachusetts USA

2. Department of Operations Research and Financial Engineering Princeton University Princeton New Jersey USA

Funder

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung

National Science Foundation

Publisher

Wiley

Subject

Applied Mathematics,Economics and Econometrics,Social Sciences (miscellaneous),Finance,Accounting

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/mafi.12365

Reference50 articles.

1. Generating synthetic data in finance

2. Deep Neural Networks Algorithms for Stochastic Control Problems on Finite Horizon: Numerical Applications

3. Estimating processes in adapted Wasserstein distance

4. Computational aspects of robust optimized certainty equivalents and option pricing

5. Bartl D. &Mendelson S.(2021).On Monte‐Carlo methods in convex stochastic optimization.arXiv preprint: 2101.07794.

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3. Deep treasury management for banks;Frontiers in Artificial Intelligence;2023-03-22

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