Stochastic Control and Games in Intraday Markets

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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.