Deep Hedging of Derivatives Using Reinforcement Learning
21 Pages Posted: 27 Jan 2020 Last revised: 21 May 2020
Date Written: December 20, 2019
This paper investigates how reinforcement learning can be used to derive optimal hedging strategies for derivatives. We assume that the objective is to minimize a function equal to the mean hedging cost plus a constant times the standard deviation of the hedging cost. The paper illustrates the approach by showing the difference between using delta hedging and optimal hedging for a short position in a call option when there are transaction costs. Two situations are considered. In the first, the asset price follows a geometric Brownian motion. In the second, the asset price follows a stochastic volatility process. The paper extends the standard reinforcement learning approach by using two different Q-functions.
Keywords: Deep hedging, derivatives, reinforcement learning
JEL Classification: C45, G10
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