RT Journal Article
SR Electronic
T1 Dynamic Replication and Hedging: <em>A Reinforcement Learning Approach</em>
JF The Journal of Financial Data Science
FD Institutional Investor Journals
SP 159
OP 171
DO 10.3905/jfds.2019.1.1.159
VO 1
IS 1
A1 Petter N. Kolm
A1 Gordon Ritter
YR 2019
UL https://pm-research.com/content/1/1/159.abstract
AB The authors of this article 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, the authors 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.TOPICS: Options, big data/machine learning, portfolio management/multi-asset allocation