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 | Size | Format | |
|---|---|---|---|---|
| 2022_Hou-Krabichler-Wunsch_Deep-partial-hedging_jrfm.pdf | 600.17 kB | Adobe PDF |  View/Open |
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