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Presented By: Department of Mathematics

Financial/Actuarial Mathematics

Algorithmic Trading with Partial Information and Learning

Algorithmic trading problems are often posed assuming the agent knows completely the stochastic model which drives prices and other state variables. Here, we analysis the optimal trading decisions of an agent who is exposed to prices that evolve according to an unknown jump-diffusion. We show that the resulting optimal stochastic control problem with partial information reduces to one with full information, solve the filtered control problem in analytic closed-form, and show how the optimal trading decisions are modified by the uncertainty. Several numerical experiments are presented to illustrate how algorithmic trading strategies are modified.

[ This is joint work with Philippe Casgrain, U. Toronto. ] Speaker(s): Sebastian Jaimungal (University of Toronto)

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