论文标题
制造商费用可以阻止市场构成算法合作吗？
Can maker-taker fees prevent algorithmic cooperation in market making?
论文作者
论文摘要
在半现实的市场模拟器中，独立的强化学习算法可能会促进做市商即使没有沟通，也可以保持广泛的差距。这种意外的结果挑战了当前的反托拉斯法律框架。我们研究制造商费用模型在防止通过算法合作的有效性。在将市场制作建模为重复的通用游戏之后，我们通过实验表明，净交易成本与制造商回扣之间的关系不一定是单调的。除了收取收费费的上限外，我们还可能需要对制造商回扣的下限来破坏合作稳定。我们还考虑了Taker-Maker模型以及中价波动率，库存风险和代理数量的影响。
In a semi-realistic market simulator, independent reinforcement learning algorithms may facilitate market makers to maintain wide spreads even without communication. This unexpected outcome challenges the current antitrust law framework. We study the effectiveness of maker-taker fee models in preventing cooperation via algorithms. After modeling market making as a repeated general-sum game, we experimentally show that the relation between net transaction costs and maker rebates is not necessarily monotone. Besides an upper bound on taker fees, we may also need a lower bound on maker rebates to destabilize the cooperation. We also consider the taker-maker model and the effects of mid-price volatility, inventory risk, and the number of agents.
