Stochastic Control and Games in Intraday Markets

日内市场中的随机控制和博弈

• 批准号: RGPIN-2018-05705
• 负责人: Jaimungal, Sebastian
• 金额: $ 5.97万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=765685
• 关键词: Stochastic; Control; Games; Intraday; Markets

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.

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，养老金计划考虑大量异质交易者为降低风险而采取的行动；纳入驱动市场的潜在信息；以及了解如何处理模型错误指定。同样，监管机构需要研究如何最好地管理和规范交易者，以避免例如市场操纵和/或小型闪崩。 该提案旨在通过观察经验行为来提供对日内金融市场急需的洞察，以试图优化某些目标（例如风险回报交易者）。一般来说，RL 产生的结果对于监管者和交易者来说是难以解释的。然而，基于模型的方法虽然产生了财务上良好的结果，但过于僵化。我建议将这两条完全独立的研究结合起来，以便监管机构和交易者可以确保建议是数据驱动的， 预期影响 该研究议程将对我们对日内市场的理解产生重大影响，同时也会对 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受益于我在此提出的研究议程。监管机构将从结果产生的见解中受益，因为我的目的是强调哪些规则可以减轻市场操纵等风险 和迷你闪光灯崩溃。