Pricing and Hedging American-Style Options with Deep Learning

Sebastian Becker, Patrick Cheridito and Arnulf Jentzen
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Sebastian Becker: RiskLab, ETH Zurich, 8092 Zurich, Switzerland
Patrick Cheridito: RiskLab, ETH Zurich, 8092 Zurich, Switzerland
Arnulf Jentzen: Faculty of Mathematics and Computer Science, University of Münster, 48149 Münster, Germany

JRFM, 2020, vol. 13, issue 7, 1-12

Abstract: 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.

Keywords: American option; Bermudan option; optimal stopping; lower bound; upper bound; hedging strategy; deep neural network (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

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