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Mean field game of controls and an application to trade crowding

机译:控件的平均现场游戏及其在交易拥挤中的应用

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In this paper we formulate the now classical problem of optimal liquidation (or optimal trading) inside a mean field game (MFG). This is a noticeable change since usually mathematical frameworks focus on one large trader facing a “background noise” (or “mean field”). In standard frameworks, the interactions between the large trader and the price are a temporary and a permanent market impact terms, the latter influencing the public price. In this paper the trader faces the uncertainty of fair price changes too but not only. He also has to deal with price changes generated by other similar market participants, impacting the prices permanently too, and acting strategically. Our MFG formulation of this problem belongs to the class of “extended MFG”, we hence provide generic results to address these “MFG of controls”, before solving the one generated by the cost function of optimal trading. We provide a closed form formula of its solution, and address the case of “heterogenous preferences” (when each participant has a different risk aversion). Last but not least we give conditions under which participants do not need to instantaneously know the state of the whole system, but can “learn” it day after day, observing others’ behaviors.
机译:在本文中,我们制定了平均场博弈(MFG)中最优清算(或最优交易)的经典问题。这是一个引人注目的变化,因为通常数学框架将重点放在面对“背景噪音”(或“均值场”)的一个大型交易商上。在标准框架中,大型交易者和价格之间的相互作用是暂时的和永久的市场影响条件,后者会影响公共价格。在本文中,交易者也面临公平价格变动的不确定性,不仅如此。他还必须应对其他类似市场参与者产生的价格变化,也永久性地影响价格,并采取战略行动。我们针对此问题的MFG公式属于“扩展MFG”类别,因此,在解决由最优交易的成本函数产生的问题之前,我们提供通用结果来解决这些“ MFG of control”。我们提供其解决方案的封闭式公式,并解决“异类偏好”的情况(当每个参与者的风险规避不同时)。最后但并非最不重要的一点是,我们提供了一些条件,使参与者不必立即了解整个系统的状态,而可以日复一日地“学习”它,观察他人的行为。

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