用强化学习与点过程建模市场，提升做市商在高波动下的报价稳定性。
ARL-Based Multi-Action Market Making with Hawkes Processes and Variable Volatility
- 引入霍克斯过程与对抗强化学习，模拟真实市场的自激行为。
- 低波动训练的策略在高波动下仍能92%时间提供双边报价。
- 适合研究智能做市、高频交易系统设计的研究者和从业者。
我们通过整合对抗强化学习（ARL）、霍克斯过程与可变波动率，拓展做市商（MMs）的动作空间，以改进做市策略。为提升策略的适应性与鲁棒性——支持始终报价、单边报价或不报价——我们从常用的泊松过程转向更符合真实市场动态的霍克斯过程，以捕捉自激行为。在波动率为2和200的条件下训练并评估策略。结果表明，在低波动环境中训练的四动作做市商能有效适应高波动环境，维持稳定表现，至少92%的时间提供双边报价。这说明引入灵活报价机制与真实市场仿真显著提升了做市策略的有效性。
原文摘要 · Abstract (English)
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.
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