Hedging American Put Options with Deep Reinforcement Learning: Appendix A

The appendix contains supplementary data tables detailing the final profit and loss (P&L) statistics for both the DRL agent and BS Delta strategies across various options. It also lists the asset paths used for testing, providing essential context and additional results that support the findings of the main paper.


This content originally appeared on HackerNoon and was authored by Economic Hedging Technology

  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

Appendix A

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:::info Authors:

(1) Reilly Pickard, Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON M5S 3G8, Canada (reilly.pickard@mail.utoronto.ca);

(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.

:::

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

:::

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This content originally appeared on HackerNoon and was authored by Economic Hedging Technology


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