Advancements in computing capabilities have enabled machine learning algorithms to learn directly from large amounts of data. Deep reinforcement learning is a particularly powerful method that uses agents to learn by interacting with an environment of data. Although many traders and investment managers rely on traditional statistical and stochastic methods to price assets and develop trading and hedging strategies, deep reinforcement learning has proven to be an effective method to learn optimal policies for pricing and hedging. Machine learning removes the need for various parametric assumptions about underlying market dynamics by learning directly from data. This research examines the use of machine learning methods to develop a data-driven method of derivatives pricing and dynamic hedging. Nevertheless, machine learning methods like reinforcement learning require an abundance of data to learn. We explore the implementation of a generative adversarial network-based approach to generate realistic market data from past historical data. This data is used to train the reinforcement learning framework and evaluate its robustness. The results demonstrate the efficacy of deep reinforcement learning methods to price derivatives and hedge positions in the proposed systematic GAN-based market simulation framework.
Our model is composed of the following components:
The models are tested on the following datasets:
They are benchmarked against the following models:
The results of our model are shown below:
As figures indicate, we achieved a similar performance as SAN for peptides datasets, which is better than other common Message-passing GNNs. Yet, we are way more efficient than SAN.
As our model is not performing as well on the resampled citation datasets, we proposes the following potential causes:
There's a lot of excellent work that was introduced around the same time as ours.
@article{samson-thesis
author = {Qian, Samson},
title = {Multi-Agent Deep Reinforcement Learning and GAN-Based Market Simulation for Derivatives Pricing and Dynamic Hedging},
journal = {MIT},
year = {2023},
}