Deep Hedging of Derivatives Using Reinforcement Learning
Prof. John Hull, Senior Fellow, GRI and Joseph L. Rotman, School of Management, University of Toronto
Jay Cao, Joseph L. Rotman, School of Management, University of Toronto
Jackie Chen, Joseph L. Rotman, School of Management, University of Toronto
Zissis Poulos, Joseph L. Rotman, School of Management, University of Toronto
One of the areas in finance where machine learning is having an impact is the hedging of derivative portfolios. If there were no transaction costs, one strategy would be to zero out all the Greek letters (delta, gamma, vega, etc.). But in practice, a trader must follow a partial hedging strategy that reflects trade-offs between risk reduction and transaction costs.
In its first foray into this area, Rotman’s research center, FinHub, considered only delta hedging. It showed that, in both a Black-Scholes environment and a stochastic volatility environment, it is possible to use reinforcement learning to develop strategies reflecting a specified trade-off between the mean and standard deviation of profits. An optimal delta-hedging strategy which included transaction costs is derived using a machine learning approach. Insights into a number of key framework decisions are discussed in the following paper.
Overview provided by:
Senior Research Fellow, Global Risk Institute
Maple Financial Group Professor in Derivatives and Risk Management, University of Toronto
Academic Director, Rotman Financial Innovation Hub
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