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Hedging American Put Options with Deep Reinforcement Learning: Referencesby@hedging
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Hedging American Put Options with Deep Reinforcement Learning: References

by Economic Hedging TechnologyOctober 30th, 2024
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The references section presents a thorough compilation of all sources cited in the study, providing foundational and contemporary literature that supports the exploration of deep reinforcement learning (DRL) in hedging American put options. This resource aids readers in further investigating the theoretical and practical aspects of the research.
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  1. Abstract

  2. Introduction

    Background

    Reinforcement Learning

    Similar Work

  3. Methodology

    DRLAgent Design

  4. Training Procedures

  5. Testing Procedures

  6. Results

  7. SABR Experiments

  8. Conclusions

  9. Appendix A

  10. References

REFERENCES

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Cao, Jay, Jacky Chen, John Hull, and Zissis Poulos. 2021. “Deep Hedging of Derivatives Using Reinforcement Learning.” The Journal of Financial Data Science 3 (1): 10–27. https://doi.org/10.3905/jfds.2020.1.052.


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Authors:

(1) Reilly Pickard, Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON M5S 3G8, Canada ([email protected]);

(2) Finn Wredenhagen, Ernst & Young LLP, Toronto, ON, M5H 0B3, Canada;

(3) Julio DeJesus, Ernst & Young LLP, Toronto, ON, M5H 0B3, Canada;

(4) Mario Schlener, Ernst & Young LLP, Toronto, ON, M5H 0B3, Canada;

(5) Yuri Lawryshyn, Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON M5S 3E5, Canada.


This paper is available on arxiv under CC BY-NC-SA 4.0 Deed (Attribution-Noncommercial-Sharelike 4.0 International) license.