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

Hans Buehler, Lukas Gonon, Josef Teichmann, Ben Wood, Baranidharan Mohan and Jonathan Kochems
Additional contact information
Hans Buehler: JP Morgan
Lukas Gonon: ETH Zurich
Josef Teichmann: ETH Zurich; Swiss Finance Institute
Ben Wood: JP Morgan Chase
Baranidharan Mohan: JP Morgan
Jonathan Kochems: JP Morgan

No 19-80, Swiss Finance Institute Research Paper Series from Swiss Finance Institute

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.

Keywords: Reinforcement Learning; Imperfect Hedging; Derivatives Pricing; Derivatives Hedging; Deep Learning (search for similar items in EconPapers)
JEL-codes: C58 C61 (search for similar items in EconPapers)
Pages: 14 pages
Date: 2019-05
New Economics Papers: this item is included in nep-big, nep-cmp and nep-fmk
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Citations: View citations in EconPapers (17)

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