ARL-Based Multi-Action Market Making with Hawkes Processes and Variable Volatility
This work addresses the need for more robust and adaptive market-making strategies in financial trading, though it appears incremental by combining existing techniques.
The paper tackled the problem of improving market-making strategies by integrating adversarial reinforcement learning, Hawkes processes, and variable volatility, and found that a 4-action market maker trained in low volatility adapts well to high volatility, maintaining stable performance and providing two-sided quotes at least 92% of the time.
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.