Full Text Available
Note: Clicking the button above will open the full text document at the original institutional repository in a new window.
Investigating the use of machine learning to value contingent claims
Dissertation (MSc (Financial Engineering))--University of Pretoria, 2024.
Saved in:
| Other Authors: | |
|---|---|
| Format: | Thesis |
| Language: | English |
| Published: |
University of Pretoria
2025
|
| Subjects: | |
| Tags: |
No Tags, Be the first to tag this record!
|
| _version_ | 1867613648837410816 |
|---|---|
| access_status_str | Open Access |
| author2 | Mare, Eben |
| author_browse | Mare, Eben |
| author_facet | Mare, Eben |
| collection | Thesis |
| dc_rights_str_mv | © 2023 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
| description | Dissertation (MSc (Financial Engineering))--University of Pretoria, 2024. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/100877 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:39:29.475Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/100877 Investigating the use of machine learning to value contingent claims Mare, Eben sriya.beharie@gmail.com Beharie, Sriya UCTD Sustainable Development Goals (SDGs) American-style options European-style option Machine learning Artificial neural networks Deep neural networks Dissertation (MSc (Financial Engineering))--University of Pretoria, 2024. A relevant area of finance that has gained traction in recent years is the use of machine learning methods with traditional approaches for pricing European and American options. This dissertation investigates the Cox-Ross-Rubinstein binomial model, the Black-Scholes model, and advanced neural network structures, including artificial neural networks and deep neural networks. By using the Black-Scholes model as a benchmark for European options, and the Cox-Ross-Rubinstein and Least-Squares Monte Carlo model for American options, our research aims to evaluate the accuracy and the efficiency of artificial and deep neural networks in option pricing, in constant and volatile market environments. We show that neural networks perform comparably to traditional models, offering an alternative for financial applications. Model limitations and other areas of improvement are also considered. Mathematics and Applied Mathematics MSc (Financial Engineering) Unrestricted Faculty of Natural and Agricultural Sciences None 2025-02-13T15:35:00Z 2025-02-13T15:35:00Z 2025-04 2024-12 Dissertation * http://hdl.handle.net/2263/100877 10.25403/UPresearchdata.28409042 en © 2023 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria |
| spellingShingle | UCTD Sustainable Development Goals (SDGs) American-style options European-style option Machine learning Artificial neural networks Deep neural networks Investigating the use of machine learning to value contingent claims |
| title | Investigating the use of machine learning to value contingent claims |
| title_full | Investigating the use of machine learning to value contingent claims |
| title_fullStr | Investigating the use of machine learning to value contingent claims |
| title_full_unstemmed | Investigating the use of machine learning to value contingent claims |
| title_short | Investigating the use of machine learning to value contingent claims |
| title_sort | investigating the use of machine learning to value contingent claims |
| topic | UCTD Sustainable Development Goals (SDGs) American-style options European-style option Machine learning Artificial neural networks Deep neural networks |
| url | http://hdl.handle.net/2263/100877 |