Modelling volatility, equity risk and extremal dependence of the BRICS Stock Markets

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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.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.uri http://hdl.handle.net/11602/2286
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.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

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