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.

在这个电子市场时代，机构投资者面临许多新的挑战（例如，养老金计划解释了大量贸易商的行动，以减轻风险，以减轻风险；驱动市场的潜在信息驱动市场；并了解如何处理模型误确定的人。 ***该建议旨在通过研究经验代理，不同的信息集和先前的假设来提供急需的洞察力，以了解大量的交互代理形成市场，并将重点放在适用的结果上，这些结果可以应用于贸易商，以及某些方案的启动范围。 RL通常会产生风险交易者。但是，基于模型的方法会产生经济上的合理结果，但过于严格。我建议将这两个完全独立的线路研究结合在一起，以便监管机构和交易者可以确保建议是数据驱动的，但在财务上是合理的。工业从业人员，博士生和其他学者将受益于我在这里提出的研究议程。监管机构将受益于结果的见解，因为我的目标是强调哪些规则可以减轻市场操纵和迷你闪存崩溃等风险。