Pricing and hedging American-style options with deep learning

Sebastian Becker, Patrick Cheridito, and Arnulf Jentzen

In this paper we introduce a deep learning method for pricing and hedging American-style options. It first computes a candidate optimal stopping policy. From there it derives a lower bound for the price. Then it calculates an upper bound, a point estimate and confidence intervals. Finally, it constructs an approximate dynamic hedging strategy. We test the approach on different specifications of a Bermudan max-call option. In all cases it produces highly accurate prices and dynamic hedging strategies with small replication errors.

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Year of publication:	2020
Authors:	Becker, Sebastian ; Cheridito, Patrick ; Jentzen, Arnulf
Published in:	Journal of risk and financial management : JRFM. - Basel : MDPI, ISSN 1911-8074, ZDB-ID 2739117-6. - Vol. 13.2020, 7/158, p. 1-12
Subject:	American option \| Bermudan option \| deep neural network \| hedging strategy \| lower bound \| optimal stopping \| upper bound \| Hedging \| Optionspreistheorie \| Option pricing theory \| Optionsgeschäft \| Option trading \| Neuronale Netze \| Neural networks \| Suchtheorie \| Search theory