Optimizing Deep Reinforcement Learning for American Put Option Hedging: Deep Reinforcement Learning

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Deep Reinforcement Learning
Similar Work

3.1 Option Hedging with Deep Reinforcement Learning

3.2 Hyperparameter Analysis
Methodology

4.1 General DRL Agent Setup

4.2 Hyperparameter Experiments

4.3 Optimization of Market Calibrated DRL Agents
Results

5.1 Hyperparameter Analysis

5.2 Market Calibrated DRL with Weekly Re-Training
Conclusions

2 Deep Reinforcement Learning

The NN is trained by optimized by minimizing the difference between the output and the target value. This objective function for iteration 𝑖 is given by

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.

Optimizing Deep Reinforcement Learning for American Put Option Hedging: Deep Reinforcement Learning

Table of Links

2 Deep Reinforcement Learning

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