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Optimizing Deep Reinforcement Learning for American Put Option Hedging: Appendix

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Table of Links

Abstract and 1. Introduction

  1. Deep Reinforcement Learning

  2. Similar Work

    3.1 Option Hedging with Deep Reinforcement Learning

    3.2 Hyperparameter Analysis

  3. Methodology

    4.1 General DRL Agent Setup

    4.2 Hyperparameter Experiments

    4.3 Optimization of Market Calibrated DRL Agents

  4. Results

    5.1 Hyperparameter Analysis

    5.2 Market Calibrated DRL with Weekly Re-Training

  5. Conclusions

Appendix

References

Appendix

Table A1: Full Results from Hedging True Asset Paths


Continuation of the Table A1


Table A2: Realized Asset Paths between Sale and Maturity Dates


Authors:

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

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

(3) Y. Lawryshyn, Department of Chemical Engineering, University of Toronto, Toronto, Canada.


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


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