An Application of Generative Adversarial Networks to One-Dimensional Value-at-Risk
| dc.contributor.advisor | Mahomed, Obeid | |
| dc.contributor.author | Swallow, Rachel | |
| dc.date.accessioned | 2024-10-30T08:23:44Z | |
| dc.date.available | 2024-10-30T08:23:44Z | |
| dc.date.issued | 2024 | |
| dc.date.updated | 2024-07-09T13:04:38Z | |
| dc.description.abstract | 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. | |
| dc.identifier.apacitation | Swallow, R. (2024). <i>An Application of Generative Adversarial Networks to One-Dimensional Value-at-Risk</i>. (). ,Faculty of Science ,Department of Statistical Sciences. Retrieved from http://hdl.handle.net/11427/40653 | en_ZA |
| dc.identifier.chicagocitation | Swallow, Rachel. <i>"An Application of Generative Adversarial Networks to One-Dimensional Value-at-Risk."</i> ., ,Faculty of Science ,Department of Statistical Sciences, 2024. http://hdl.handle.net/11427/40653 | en_ZA |
| dc.identifier.citation | Swallow, R. 2024. An Application of Generative Adversarial Networks to One-Dimensional Value-at-Risk. . ,Faculty of Science ,Department of Statistical Sciences. http://hdl.handle.net/11427/40653 | en_ZA |
| dc.identifier.ris | TY - Thesis / Dissertation AU - Swallow, Rachel AB - 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. DA - 2024 DB - OpenUCT DP - University of Cape Town KW - Statistical Sciences LK - https://open.uct.ac.za PY - 2024 T1 - An Application of Generative Adversarial Networks to One-Dimensional Value-at-Risk TI - An Application of Generative Adversarial Networks to One-Dimensional Value-at-Risk UR - http://hdl.handle.net/11427/40653 ER - | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/11427/40653 | |
| dc.identifier.vancouvercitation | Swallow R. An Application of Generative Adversarial Networks to One-Dimensional Value-at-Risk. []. ,Faculty of Science ,Department of Statistical Sciences, 2024 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/40653 | en_ZA |
| dc.language.rfc3066 | Eng | |
| dc.publisher.department | Department of Statistical Sciences | |
| dc.publisher.faculty | Faculty of Science | |
| dc.subject | Statistical Sciences | |
| dc.title | An Application of Generative Adversarial Networks to One-Dimensional Value-at-Risk | |
| dc.type | Thesis / Dissertation | |
| dc.type.qualificationlevel | Masters |