Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-25472
Publication type: Article in scientific journal
Type of review: Peer review (publication)
Title: Deep partial hedging
Authors: Hou, Songyan
Krabichler, Thomas
Wunsch, Marcus
et. al: No
DOI: 10.3390/jrfm15050223
10.21256/zhaw-25472
Published in: Journal of Risk and Financial Management
Volume(Issue): 15
Issue: 5
Pages to: 223
Issue Date: 2022
Publisher / Ed. Institution: MDPI
ISSN: 1911-8066
1911-8074
Language: English
Subjects: Machine learning; Risk management; Transaction cost; Market friction; Partial hedging
Subject (DDC): 006: Special computer methods
332.6: Investment
Abstract: Using techniques from deep learning, we show that neural networks can be trained successfully to replicate the modified payoff functions that were first derived in the context of partial hedging by Föllmer and Leukert. Not only does this approach better accommodate the realistic setting of hedging in discrete time, it also allows for the inclusion of transaction costs as well as general market dynamics. It needs to be noted that, without further modifications, the approach works only if the risk aversion is beyond a certain level.
URI: https://digitalcollection.zhaw.ch/handle/11475/25472
Fulltext version: Published version
License (according to publishing contract): CC BY 4.0: Attribution 4.0 International
Departement: School of Management and Law
Organisational Unit: Institute of Wealth & Asset Management (IWA)
Appears in collections:Publikationen School of Management and Law

Files in This Item:
File Description SizeFormat 
2022_Hou-Krabichler-Wunsch_Deep-partial-hedging_jrfm.pdf600.17 kBAdobe PDFThumbnail
View/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.