Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/239245 
Year of Publication: 
2020
Citation: 
[Journal:] Journal of Risk and Financial Management [ISSN:] 1911-8074 [Volume:] 13 [Issue:] 7 [Publisher:] MDPI [Place:] Basel [Year:] 2020 [Pages:] 1-12
Publisher: 
MDPI, Basel
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.
Subjects: 
American option
Bermudan option
deep neural network
hedging strategy
lower bound
optimal stopping
upper bound
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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