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；驱动市场的潜在信息；驱动市场的潜在信息；并了解如何处理模型的误差。监管机构还需要研究如何最佳地管理和跨越企业，以避免使用和市场范围或市场范围/换取和市场，以避免和市场范围/更新。该提案旨在通过考虑优化某些目标的经验行动来提供急需的洞察力（例如，风险返回交易者，RL都会产生监管机构和贸易商很难解释的结果，但是我会完全努力地使用这些界限。但是财务上的声音。期望的影响这一研究议程将对我们对盘中市场的理解产生重大影响，同时在MFG中的发展，模型不确定性和强化学习。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。