Deep Hedging: Hedging Derivatives Under Generic Market Frictions Using Reinforcement Learning - Machine Learning Version

12 Pages Posted:

See all articles by Hans Buehler

Hans Buehler

JP Morgan

Lukas Gonon

ETH Zurich

Ben Wood

JP Morgan Chase

Josef Teichmann

ETH Zurich; Swiss Finance Institute

Baranidharan Mohan

JP Morgan

Jonathan Kochems

JP Morgan

Date Written: March 19, 2019

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

JEL Classification: C61, C58

Suggested Citation

Buehler, Hans and Gonon, Lukas and Wood, Ben and Teichmann, Josef and Mohan, Baranidharan and Kochems, Jonathan, Deep Hedging: Hedging Derivatives Under Generic Market Frictions Using Reinforcement Learning - Machine Learning Version (March 19, 2019). Available at SSRN: https://ssrn.com/abstract=

Hans Buehler (Contact Author)

JP Morgan ( email )

London
United Kingdom

Lukas Gonon

ETH Zurich ( email )

Rämistrasse 101
ZUE F7
Zürich, 8092
Switzerland

Ben Wood

JP Morgan Chase ( email )

London
United Kingdom

Josef Teichmann

ETH Zurich ( email )

Rämistrasse 101
ZUE F7
Zürich, 8092
Switzerland

HOME PAGE: http://www.math.ethz.ch/~jteichma

Swiss Finance Institute ( email )

c/o University of Geneva
40 Bd du Pont-d'Arve
CH-1211 Geneva 4
Switzerland

Baranidharan Mohan

JP Morgan ( email )

London
United Kingdom

Jonathan Kochems

JP Morgan ( email )

London
United Kingdom

Register to save articles to
your library

Register

Paper statistics

Downloads
643
Abstract Views
1,659
PlumX Metrics