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

TY - CHAP

T1 - ARL-Based Multi-Action Market Making with Hawkes Processes and Variable Volatility

AU - Wang, Ziyi

AU - Ventre, Carmine

AU - Polukarov, Maria

PY - 2024

Y1 - 2024

N2 - 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.

AB - 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.

U2 - 10.1145/3677052.3698695

DO - 10.1145/3677052.3698695

M3 - Chapter

SP - 437

EP - 444

BT - Proceedings of the 5th ACM International Conference on AI in Finance (ICAIF 2024)

ER -