Join us on October 2nd for a discussion about Dynamic Replication and Hedging: A Reinforcement Learning Approach presented by Petter Kolm.
About this Event
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 that 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.iijournals.com/content/1/1/159
Location
Manhattan Institute of Management
2 Washington Street
17th Floor
New York, NY 10004