Stochastic Control and Games in Intraday Markets

在这个电子市场时代，机构投资者面临着许多新的挑战（例如，养老金计划考虑到大量异质交易者的行动，以降低风险；纳入驱动市场的潜在信息；以及理解如何处理模型错误规范）。此外，监管机构需要研究如何最好地管理和监管交易员，以避免市场操纵和/或迷你闪电崩盘。该提案旨在通过观察实证行为，试图优化某些目标（如风险回报交易者退出），为日内金融市场提供急需的洞察力。一般来说，强化学习产生的结果很难让监管机构和交易员解释。然而，基于模型的方法可以产生财务上合理的结果，但过于僵化。我建议将这两种完全独立的研究结合起来，这样监管机构和交易员就可以确保建议是由数据驱动的，但在财务上是合理的。本研究议程将对我们对日内市场的理解产生重大影响，同时对mfg、模型不确定性和强化学习的发展产生重大影响。工业从业者、博士生和其他学者将从我在这里提出的研究议程中受益。监管机构将受益于这些结果所产生的见解，因为我的目的是强调哪些规则可以减轻市场操纵和迷你闪电崩盘等风险。

In this era of electronic markets, there are a number of new challenges faced by institutional investors (e.g., pension plans account for actions from a large number of heterogeneous traders to mitigate risks; incorporate latent information that drives markets; and understanding how to deal with model misspecification. As well, regulators need to study how to best manage and regulate traders to avoid, e.g., market manipulation and/or mini-flash crashes. This proposal aims to provide much needed insight into intraday financial markets by looking at empirical s action in an attempt to optimize some objective (such as a risk-return trader off). Generally, RL produces results that are difficult for regulators and traders to interpret. Model-based approaches, however, produce financially sound results, but are too rigid. I propose to combine these two completely separate lines research so that regulators and traders can be sure that recommendations are data-driven, but financially sound.Anticipated Impact This research agenda will have significant impact in our understanding of intraday markets and, simultaneously, developments in MFGs, model uncertainty, and reinforcement learning. Industrial practitioners, PhD students, and other academics, will benefit from the research agenda I propose here. Regulators will benefit from the insights stemming from the results, as I aim to highlight what rules can mitigate risks such as market manipulation and mini flash crashes.