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Evaluating value at risk models: an application to the Johannesburg Stock Exchange
The management of market risk is an essential determinant of the stability of a financial institution, and by extension, of the overall financial system. There are various variables which impact on the accuracy of a market risk management system. For various reasons which are discussed in this study...
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| Format: | Thesis |
| Language: | English |
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Department of Finance and Tax
2016
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| _version_ | 1867614343699365888 |
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| access_status_str | Open Access |
| author | Chotee, Deepika |
| author2 | Toerien, Francois |
| author_browse | Chotee, Deepika Toerien, Francois |
| author_facet | Toerien, Francois Chotee, Deepika |
| author_sort | Chotee, Deepika |
| collection | Thesis |
| description | The management of market risk is an essential determinant of the stability of a financial institution, and by extension, of the overall financial system. There are various variables which impact on the accuracy of a market risk management system. For various reasons which are discussed in this study, Value at Risk (VaR) is used as a measure of market risk. VaR has certain key features which make it adaptable to several types of scenarios in order to provide a measure of market risk. In order to assess these features of VaR, this study evaluates VaR using a range of techniques. This study analyses the performance of some of the most popular VaR models using the JSE ALSI's total daily returns. The VaR estimates were calculated for each model using varying parameters for confidence level, risk horizon, distributional assumptions and other variables. The study evaluates the relative accuracy of each model analysed, over specific subsets of the data set under consideration, and performs five different backtests to determine the accuracy of each model. The aim of this analysis is to identify the model most suited to predicting VaR in the South African environment. A key feature of this study is that it includes data during and after the financial crisis, and can, therefore, model the respective volatility characteristics of the data during this period. The results of the analysis indicate that the asymmetric GARCH models outperform the other models over both the full sample period and the crisis and post-crisis subperiods, and that the t distribution assumption produces more accurate forecasts. This implies that such models are better suited to capturing the effects of volatility for data with these characteristics. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/18625 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:50:32.538Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2016 |
| publishDateRange | 2016 |
| publishDateSort | 2016 |
| 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:11427/18625 Evaluating value at risk models: an application to the Johannesburg Stock Exchange Chotee, Deepika Toerien, Francois Kruger, Ryan Financial Management The management of market risk is an essential determinant of the stability of a financial institution, and by extension, of the overall financial system. There are various variables which impact on the accuracy of a market risk management system. For various reasons which are discussed in this study, Value at Risk (VaR) is used as a measure of market risk. VaR has certain key features which make it adaptable to several types of scenarios in order to provide a measure of market risk. In order to assess these features of VaR, this study evaluates VaR using a range of techniques. This study analyses the performance of some of the most popular VaR models using the JSE ALSI's total daily returns. The VaR estimates were calculated for each model using varying parameters for confidence level, risk horizon, distributional assumptions and other variables. The study evaluates the relative accuracy of each model analysed, over specific subsets of the data set under consideration, and performs five different backtests to determine the accuracy of each model. The aim of this analysis is to identify the model most suited to predicting VaR in the South African environment. A key feature of this study is that it includes data during and after the financial crisis, and can, therefore, model the respective volatility characteristics of the data during this period. The results of the analysis indicate that the asymmetric GARCH models outperform the other models over both the full sample period and the crisis and post-crisis subperiods, and that the t distribution assumption produces more accurate forecasts. This implies that such models are better suited to capturing the effects of volatility for data with these characteristics. 2016-04-05T11:48:59Z 2016-04-05T11:48:59Z 2014 Master Thesis Masters MCom http://hdl.handle.net/11427/18625 eng application/pdf Department of Finance and Tax Faculty of Commerce University of Cape Town |
| spellingShingle | Financial Management Chotee, Deepika Evaluating value at risk models: an application to the Johannesburg Stock Exchange |
| thesis_degree_str | Master's |
| title | Evaluating value at risk models: an application to the Johannesburg Stock Exchange |
| title_full | Evaluating value at risk models: an application to the Johannesburg Stock Exchange |
| title_fullStr | Evaluating value at risk models: an application to the Johannesburg Stock Exchange |
| title_full_unstemmed | Evaluating value at risk models: an application to the Johannesburg Stock Exchange |
| title_short | Evaluating value at risk models: an application to the Johannesburg Stock Exchange |
| title_sort | evaluating value at risk models an application to the johannesburg stock exchange |
| topic | Financial Management |
| url | http://hdl.handle.net/11427/18625 |
| work_keys_str_mv | AT choteedeepika evaluatingvalueatriskmodelsanapplicationtothejohannesburgstockexchange |