Modelling volatility, equity risk and extremal dependence of the BRICS Stock Markets
| dc.contributor.advisor | Sigauke, Caston | |
| dc.contributor.advisor | Chagwiza, Wilbert | |
| dc.contributor.advisor | Garira, Winston | |
| dc.contributor.author | Mukhodobwane, Rosinah Mphedziseni | |
| dc.date | 2021 | |
| dc.date.accessioned | 2022-09-17T18:24:50Z | |
| dc.date.available | 2022-09-17T18:24:50Z | |
| dc.date.issued | 2022-07-15 | |
| dc.description | PhD (Mathematics) | en_ZA |
| dc.description | Departmental of Mathematical and Computational Sciences | |
| dc.description.abstract | With the use of empirical data of the BRICS (Brazil, Russia, India, China, and South Africa) stock markets, this thesis focuses on solving three main nancial and investment issues involving returns volatility, risk and extremal dependence via robust statistical modelling. The rst issue involves modelling nancial returns volatility (when the true distribution is unknown) using the univariate GARCH model under the assumptions of seven error distributions. The ndings, using two of the error distributions, show that the Chinese market has the highest volatility persistence, followed by the South African, Russian, Indian and Brazilian markets in that order. For risk modelling and analysis, the ndings show that the Russian market has the highest risk level, followed by the South African, Chinese, Brazilian and Indian markets, respectively. For the extremal dependence modelling, using the bivariate point process and conditional multivariate extreme value (CMEV) models, the ndings show varied levels of low extremal dependence structure whose outcomes are highly bene cial to investors, portfolio managers and other market participants who are interested in maximising their investment returns and nancial gains. However, it is observed that the point process was able to model many more extreme observations or exceedances that contribute to the likelihood estimation and it gives more information than the threshold excess method of the CMEV model. | en_ZA |
| dc.description.sponsorship | NRF | en_ZA |
| dc.format.extent | 1 online resource (xxvi, 324 leaves) : color illustrations | |
| dc.identifier.apacitation | Mukhodobwane, R. M. (2022). <i>Modelling volatility, equity risk and extremal dependence of the BRICS Stock Markets</i>. (). . Retrieved from http://hdl.handle.net/11602/2286 | en_ZA |
| dc.identifier.chicagocitation | Mukhodobwane, Rosinah Mphedziseni. <i>"Modelling volatility, equity risk and extremal dependence of the BRICS Stock Markets."</i> ., , 2022. http://hdl.handle.net/11602/2286 | en_ZA |
| dc.identifier.citation | Mukhodobwane, R. M. (2021) Modelling volatility, equity risk and extremal dependence of the BRICS Stock Markets. University of Venda. South Africa.<http://hdl.handle.net/11602/2286>. | |
| dc.identifier.ris | TY - Thesis AU - Mukhodobwane, Rosinah Mphedziseni AB - With the use of empirical data of the BRICS (Brazil, Russia, India, China, and South Africa) stock markets, this thesis focuses on solving three main nancial and investment issues involving returns volatility, risk and extremal dependence via robust statistical modelling. The rst issue involves modelling nancial returns volatility (when the true distribution is unknown) using the univariate GARCH model under the assumptions of seven error distributions. The ndings, using two of the error distributions, show that the Chinese market has the highest volatility persistence, followed by the South African, Russian, Indian and Brazilian markets in that order. For risk modelling and analysis, the ndings show that the Russian market has the highest risk level, followed by the South African, Chinese, Brazilian and Indian markets, respectively. For the extremal dependence modelling, using the bivariate point process and conditional multivariate extreme value (CMEV) models, the ndings show varied levels of low extremal dependence structure whose outcomes are highly bene cial to investors, portfolio managers and other market participants who are interested in maximising their investment returns and nancial gains. However, it is observed that the point process was able to model many more extreme observations or exceedances that contribute to the likelihood estimation and it gives more information than the threshold excess method of the CMEV model. DA - 2022-07-15 DB - ResearchSpace DP - Univen KW - Conditional extreme value model KW - Equity markets KW - Equity-risk KW - GARCH model KW - Point process KW - Risk management KW - Volatility LK - https://univendspace.univen.ac.za PY - 2022 T1 - Modelling volatility, equity risk and extremal dependence of the BRICS Stock Markets TI - Modelling volatility, equity risk and extremal dependence of the BRICS Stock Markets UR - http://hdl.handle.net/11602/2286 ER - | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/11602/2286 | |
| dc.identifier.vancouvercitation | Mukhodobwane RM. Modelling volatility, equity risk and extremal dependence of the BRICS Stock Markets. []. , 2022 [cited yyyy month dd]. Available from: http://hdl.handle.net/11602/2286 | en_ZA |
| dc.language.iso | en | en_ZA |
| dc.rights | University of Venda | |
| dc.subject | Conditional extreme value model | en_ZA |
| dc.subject | Equity markets | en_ZA |
| dc.subject | Equity-risk | en_ZA |
| dc.subject | GARCH model | en_ZA |
| dc.subject | Point process | en_ZA |
| dc.subject | Risk management | en_ZA |
| dc.subject | Volatility | en_ZA |
| dc.subject.ddc | 332.456 | |
| dc.subject.lcsh | International finance | |
| dc.subject.lcsh | Foreign exchange rates | |
| dc.subject.lcsh | Foreign exchange | |
| dc.title | Modelling volatility, equity risk and extremal dependence of the BRICS Stock Markets | en_ZA |
| dc.type | Thesis | en_ZA |