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An Application of Generative Adversarial Networks to One-Dimensional Value-at-Risk
A generative adversarial network (GAN) is an implicit generative model made up of two neural networks. This minor dissertation applies GANs to recover target statistical distributions. GANs have a distinctive training architecture designed to create examples that reproduce target data samples. These...
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| Format: | Thesis |
| Language: | Eng |
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Department of Statistical Sciences
2024
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| _version_ | 1867613344038387712 |
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| access_status_str | Open Access |
| author | Swallow, Rachel |
| author2 | Mahomed, Obeid |
| author_browse | Mahomed, Obeid Swallow, Rachel |
| author_facet | Mahomed, Obeid Swallow, Rachel |
| author_sort | Swallow, Rachel |
| collection | Thesis |
| description | A generative adversarial network (GAN) is an implicit generative model made up of two neural networks. This minor dissertation applies GANs to recover target statistical distributions. GANs have a distinctive training architecture designed to create examples that reproduce target data samples. These models have been applied successfully in high-dimensional domains such as natural image generation and processing. Much less research has been reported on applications with low dimensional distributions, where properties of GANs may be better identified and understood. One such area in finance is the use of GANs for estimating value-at-risk (VaR). Through this financial application, this dissertation introduces readers to the concepts and practical implementations of GAN variants to generate one-dimensional portfolio returns over a single period. Large portions of the discussions should be accessible to anyone who has an entry-level statistics course. It is aimed at data science or finance students looking to better their understanding of GANs and the potential of these models for other financial applications. Five GAN loss variants are introduced and three of these models are practically implemented to estimate VaR. The GAN estimates are compared to more traditional VaR estimation techniques and all models are backtested. Most GAN models trained in this dissertation are able to capture key features of each of the distributions, however these models do not outperform historical VaR estimates. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/40653 |
| institution | University of Cape Town (South Africa) |
| language | Eng |
| last_indexed | 2026-06-10T12:34:39.078Z |
| 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:11427/40653 An Application of Generative Adversarial Networks to One-Dimensional Value-at-Risk Swallow, Rachel Mahomed, Obeid Statistical Sciences A generative adversarial network (GAN) is an implicit generative model made up of two neural networks. This minor dissertation applies GANs to recover target statistical distributions. GANs have a distinctive training architecture designed to create examples that reproduce target data samples. These models have been applied successfully in high-dimensional domains such as natural image generation and processing. Much less research has been reported on applications with low dimensional distributions, where properties of GANs may be better identified and understood. One such area in finance is the use of GANs for estimating value-at-risk (VaR). Through this financial application, this dissertation introduces readers to the concepts and practical implementations of GAN variants to generate one-dimensional portfolio returns over a single period. Large portions of the discussions should be accessible to anyone who has an entry-level statistics course. It is aimed at data science or finance students looking to better their understanding of GANs and the potential of these models for other financial applications. Five GAN loss variants are introduced and three of these models are practically implemented to estimate VaR. The GAN estimates are compared to more traditional VaR estimation techniques and all models are backtested. Most GAN models trained in this dissertation are able to capture key features of each of the distributions, however these models do not outperform historical VaR estimates. 2024-10-30T08:23:44Z 2024-10-30T08:23:44Z 2024 2024-07-09T13:04:38Z Thesis / Dissertation Masters http://hdl.handle.net/11427/40653 Eng application/pdf Department of Statistical Sciences Faculty of Science |
| spellingShingle | Statistical Sciences Swallow, Rachel An Application of Generative Adversarial Networks to One-Dimensional Value-at-Risk |
| thesis_degree_str | Master's |
| title | An Application of Generative Adversarial Networks to One-Dimensional Value-at-Risk |
| title_full | An Application of Generative Adversarial Networks to One-Dimensional Value-at-Risk |
| title_fullStr | An Application of Generative Adversarial Networks to One-Dimensional Value-at-Risk |
| title_full_unstemmed | An Application of Generative Adversarial Networks to One-Dimensional Value-at-Risk |
| title_short | An Application of Generative Adversarial Networks to One-Dimensional Value-at-Risk |
| title_sort | application of generative adversarial networks to one dimensional value at risk |
| topic | Statistical Sciences |
| url | http://hdl.handle.net/11427/40653 |
| work_keys_str_mv | AT swallowrachel anapplicationofgenerativeadversarialnetworkstoonedimensionalvalueatrisk AT swallowrachel applicationofgenerativeadversarialnetworkstoonedimensionalvalueatrisk |