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.
