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Banking regulation: a bayesian network approach to risk management
The ever-evolving regulation surrounding banks and market risk, coupled with increased computing power, make for favourable conditions in employing machine learning techniques to estimate and forecast market risk metrics such as value at risk (VaR) and expected shortfall (ES). This study consists of...
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| Format: | Thesis |
| Language: | English English |
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Department of Finance and Tax
2025
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| _version_ | 1867613208635768832 |
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| access_status_str | Open Access |
| author | Gross, Eden |
| author2 | Kruger, Ryan |
| author_browse | Gross, Eden Kruger, Ryan |
| author_facet | Kruger, Ryan Gross, Eden |
| author_sort | Gross, Eden |
| collection | Thesis |
| description | The ever-evolving regulation surrounding banks and market risk, coupled with increased computing power, make for favourable conditions in employing machine learning techniques to estimate and forecast market risk metrics such as value at risk (VaR) and expected shortfall (ES). This study consists of three sections. First, this study comprehensively examines the performance of various market risk models when producing VaR and ES, and their stressed counterparts, using Standard and Poor's (S&P) 5 00 index returns from 1991 to 2020. The initial results show that autoregressive models are the most accurate of the traditional market risk models. Second, the first section's results are then used as the basis against which a novel and comprehensive Bayesian network (BN) methodology for producing VaR and ES forecasts, and those of their stressed counterparts, is assessed in the context of banking regulations, using four learning algorithms. The forecasts generated by the BNs are not found to offer any improved accuracy when incorporated into the market risk metric calculations, primarily due to the limited weight of the forecast in the return distribution relative to the historical returns in the return probability density function. Finally, a novel integrated forecast dynamic Bayesian network (IFDBN) methodology is developed, whereby, for each metric, the best -in-class autoregressive model and the best-in-class BN learning algorithm are coupled to produce market risk forecasts. The results of the IFDBNs are mixed, with the stressed ES metric IFDBN being the only IFDBN to produce more accurate forecasts relative to its traditional autoregressive counterpart. While certain market risk metrics may benefit from using IFDBNs in the forecasting process, this result is not universal, and the risk practitioner must evaluate the usefulness of IFDBNs on a case-by-case basis. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/42291 |
| institution | University of Cape Town (South Africa) |
| language | English eng |
| last_indexed | 2026-06-10T12:32:29.432Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Department of Finance and Tax |
| publisherStr | Department of Finance and Tax |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/42291 Banking regulation: a bayesian network approach to risk management Gross, Eden Kruger, Ryan Toerien, Francois Bayesian Network Risk Management The ever-evolving regulation surrounding banks and market risk, coupled with increased computing power, make for favourable conditions in employing machine learning techniques to estimate and forecast market risk metrics such as value at risk (VaR) and expected shortfall (ES). This study consists of three sections. First, this study comprehensively examines the performance of various market risk models when producing VaR and ES, and their stressed counterparts, using Standard and Poor's (S&P) 5 00 index returns from 1991 to 2020. The initial results show that autoregressive models are the most accurate of the traditional market risk models. Second, the first section's results are then used as the basis against which a novel and comprehensive Bayesian network (BN) methodology for producing VaR and ES forecasts, and those of their stressed counterparts, is assessed in the context of banking regulations, using four learning algorithms. The forecasts generated by the BNs are not found to offer any improved accuracy when incorporated into the market risk metric calculations, primarily due to the limited weight of the forecast in the return distribution relative to the historical returns in the return probability density function. Finally, a novel integrated forecast dynamic Bayesian network (IFDBN) methodology is developed, whereby, for each metric, the best -in-class autoregressive model and the best-in-class BN learning algorithm are coupled to produce market risk forecasts. The results of the IFDBNs are mixed, with the stressed ES metric IFDBN being the only IFDBN to produce more accurate forecasts relative to its traditional autoregressive counterpart. While certain market risk metrics may benefit from using IFDBNs in the forecasting process, this result is not universal, and the risk practitioner must evaluate the usefulness of IFDBNs on a case-by-case basis. 2025-11-21T07:25:35Z 2025-11-21T07:25:35Z 2025 2025-11-21T07:22:34Z Thesis / Dissertation Doctoral PhD http://hdl.handle.net/11427/42291 en eng application/pdf Department of Finance and Tax Faculty of Commerce University of Cape Town |
| spellingShingle | Bayesian Network Risk Management Gross, Eden Banking regulation: a bayesian network approach to risk management |
| thesis_degree_str | Doctoral |
| title | Banking regulation: a bayesian network approach to risk management |
| title_full | Banking regulation: a bayesian network approach to risk management |
| title_fullStr | Banking regulation: a bayesian network approach to risk management |
| title_full_unstemmed | Banking regulation: a bayesian network approach to risk management |
| title_short | Banking regulation: a bayesian network approach to risk management |
| title_sort | banking regulation a bayesian network approach to risk management |
| topic | Bayesian Network Risk Management |
| url | http://hdl.handle.net/11427/42291 |
| work_keys_str_mv | AT grosseden bankingregulationabayesiannetworkapproachtoriskmanagement |