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Deep hedging in incomplete markets
This dissertation presents an extensive analysis of the neural network approximation of mean-variance hedging with a comparison between the current neural network approaches and the theoretical solutions. These theoretical solutions provide a simulation-based performance benchmark for this compariso...
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| Format: | Thesis |
| Language: | Eng |
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Department of Statistical Sciences
2024
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| _version_ | 1867614095797125120 |
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| access_status_str | Open Access |
| author | Stangroom, Jake |
| author2 | Mavuso, Melusi |
| author_browse | Mavuso, Melusi Stangroom, Jake |
| author_facet | Mavuso, Melusi Stangroom, Jake |
| author_sort | Stangroom, Jake |
| collection | Thesis |
| description | This dissertation presents an extensive analysis of the neural network approximation of mean-variance hedging with a comparison between the current neural network approaches and the theoretical solutions. These theoretical solutions provide a simulation-based performance benchmark for this comparison. Furthermore, this dissertation implements a financial market generator which allows for a realistic performance analysis based on both real and pseudo-real data; whereby, deep hedging is shown to offer highly competitive industry performance. Finally, the dissertation shows that deep hedging is effective for other quadratic criterion such as those similar to local risk-minimisation techniques. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/40641 |
| institution | University of Cape Town (South Africa) |
| language | Eng |
| last_indexed | 2026-06-10T12:46:36.120Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Department of Statistical Sciences |
| publisherStr | Department of Statistical Sciences |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/40641 Deep hedging in incomplete markets Stangroom, Jake Mavuso, Melusi Statistical Sciences This dissertation presents an extensive analysis of the neural network approximation of mean-variance hedging with a comparison between the current neural network approaches and the theoretical solutions. These theoretical solutions provide a simulation-based performance benchmark for this comparison. Furthermore, this dissertation implements a financial market generator which allows for a realistic performance analysis based on both real and pseudo-real data; whereby, deep hedging is shown to offer highly competitive industry performance. Finally, the dissertation shows that deep hedging is effective for other quadratic criterion such as those similar to local risk-minimisation techniques. 2024-10-29T10:13:21Z 2024-10-29T10:13:21Z 2024 2024-07-09T13:01:21Z Thesis / Dissertation Masters MSc http://hdl.handle.net/11427/40641 Eng application/pdf Department of Statistical Sciences Faculty of Science |
| spellingShingle | Statistical Sciences Stangroom, Jake Deep hedging in incomplete markets |
| thesis_degree_str | Master's |
| title | Deep hedging in incomplete markets |
| title_full | Deep hedging in incomplete markets |
| title_fullStr | Deep hedging in incomplete markets |
| title_full_unstemmed | Deep hedging in incomplete markets |
| title_short | Deep hedging in incomplete markets |
| title_sort | deep hedging in incomplete markets |
| topic | Statistical Sciences |
| url | http://hdl.handle.net/11427/40641 |
| work_keys_str_mv | AT stangroomjake deephedginginincompletemarkets |