Deep Hedging: Hedging Derivatives Under Generic Market Frictions Using Reinforcement Learning

Abstract

This article discusses a new application of reinforcement learning: to the problem of hedging a portfolio of “over-the-counter” derivatives under under market frictions such as trading costs and liquidity constraints. It is an extended version of our recent work https://www.ssrn.com/abstract=3120710, here using notation more common in the machine learning literature.

The objective is to maximize a non-linear risk-adjusted return function by trading in liquid hedging instruments such as equities or listed options. The approach presented here is the first efficient and model-independent algorithm which can be used for such problems at scale.

There are now some code examples on GitHub.

Keywords: Reinforcement Learning, Imperfect Hedging, Derivatives Pricing, Derivatives Hedging, Deep Learning

JEL Classification: C61, C58