Petter Kolm, New York University
In this talk, we address the problem of how to optimally hedge an options book in a practical setting, where trading decisions are discrete and trading costs can be nonlinear and difficult to model.
Based on reinforcement learning (RL), a well-established machine learning technique, we propose a model that is flexible, accurate, and very promising for real-world applications. A key strength of the RL approach is that it does not make any assumptions about the form of trading cost. RL learns the minimum variance hedge subject to whatever transaction cost function one provides. All it needs is a good simulator in which transaction costs and options prices are simulated accurately.
This is joint work with Gordon Ritter.
Published paper: https://jfds.pm-research.com/content/1/1/159