# ARL-Based Multi-Action Market Making with Hawkes Processes and Variable Volatility

arXiv: https://arxiv.org/abs/2508.16589 · HTML: https://mlsanity.com/paper/2508.16589/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Adversarial robustness and RL in finance, one of 15 directions in Deep reinforcement learning methods.
- Builds on 2 of its 33 references that are in the map.
- First posted on arXiv 7 Aug 2025.

## Placed in

- Deep reinforcement learning methods / [Adversarial robustness and RL in finance](https://mlsanity.com/deeprl/adversarial-robustness-and-rl-in-finance/)

## Builds on (most cited first, 2 of 2)

- [Market Making via Reinforcement Learning](https://mlsanity.com/paper/1804.04216/) (2018)
- [Robust Market Making via Adversarial Reinforcement Learning](https://mlsanity.com/paper/2003.01820/) (2020)

## Built on by (most cited first, 2 of 2)

- [Multi-Agent Reinforcement Learning for Market Making: Competition without Collusion](https://mlsanity.com/paper/2510.25929/) (2025)
- [Robust Market Making with Hawkes Order Flow and Price Impact via Adversarial Reinforcement Learning](https://mlsanity.com/paper/2609.22785/) (2026)

## Abstract (arXiv)

We advance market-making strategies by integrating Adversarial Reinforcement Learning (ARL), Hawkes Processes, and variable volatility levels while also expanding the action space available to market makers (MMs). To enhance the adaptability and robustness of these strategies -- which can quote always, quote only on one side of the market or not quote at all -- we shift from the commonly used Poisson process to the Hawkes process, which better captures real market dynamics and self-exciting behaviors. We then train and evaluate strategies under volatility levels of 2 and 200. Our findings show that the 4-action MM trained in a low-volatility environment effectively adapts to high-volatility conditions, maintaining stable performance and providing two-sided quotes at least 92% of the time. This indicates that incorporating flexible quoting mechanisms and realistic market simulations significantly enhances the effectiveness of market-making strategies.