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: $ 2.99万
• 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: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=688369
• 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 eous agents, differing information sets, and prior assumptions. The goal is to understand how large number of interacting agents form markets, and the focus will be on applicable results that can be applied to inform traders, as well as, regulators on how to mitigate risks.***Machine Learning squo;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.

在这个电子市场的时代，机构投资者面临着许多新的挑战（例如，养老金计划说明了大量异质交易者为降低风险而采取的行动;纳入了驱动市场的潜在信息;并了解如何处理模型错误。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，例如，市场操纵和/或小型闪电崩盘。*这个建议的目的是提供急需的洞察日内金融市场通过看经验的代理人，不同的信息集，和先验假设。目标是了解大量相互作用的代理人如何形成市场，重点是可用于通知交易者的适用结果，以及监管机构如何减轻风险。机器学习的动作，试图优化某些目标（如风险回报交易员关闭）。一般来说，RL产生的结果很难让监管机构和交易员解释。然而，基于模型的方法产生了财务上合理的结果，但过于僵化。我建议将这两条完全独立的研究路线联合收割机结合起来，这样监管机构和交易员就可以确保推荐是数据驱动的，但在财务上是合理的。预期影响 * 本研究议程将对我们理解日内市场产生重大影响，同时也将对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，将受益于我在这里提出的研究议程。监管机构将从研究结果中获益，因为我的目标是强调哪些规则可以减轻市场操纵和小型闪电崩盘等风险。