Artikel

Deep partial hedging

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.

Language
Englisch

Bibliographic citation
Journal: Journal of Risk and Financial Management ; ISSN: 1911-8074 ; Volume: 15 ; Year: 2022 ; Issue: 5 ; Pages: 1-5

Classification
Management
Subject
machine learning
market frictions
partial hedging
risk management
transaction costs

Event
Geistige Schöpfung
(who)
Hou, Songyan
Krabichler, Thomas
Wunsch, Marcus
Event
Veröffentlichung
(who)
MDPI
(where)
Basel
(when)
2022

DOI
doi:10.3390/jrfm15050223
Handle
Last update
10.03.2025, 11:42 AM CET

Data provider

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Object type

  • Artikel

Associated

  • Hou, Songyan
  • Krabichler, Thomas
  • Wunsch, Marcus
  • MDPI

Time of origin

  • 2022

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