Multivariate analysis of the BRICS financial markets.
The co-movements and integration of financial markets has been a subject of great concern among many researchers and economists due to an interest in the impacts of stock market integration in terms of international portfolio diversification, asset allocation and asset pricing efficiency. Understanding the interdependence among financial markets is thus of immense importance especially to investors and stakeholders in making viable decisions, managing risks and monitoring portfolio performances. In this thesis, we investigated the levels of interdependence and dynamic linkages among the five emerging economies well known as the BRICS: Brazil, Russia, India, China and South Africa, using a Vector autoregressive (VAR), univariate GARCH(1,1) and multivariate GARCH models. Our data sample consisted of the BRICS weekly returns from the period of January 2000 to December 2012. We used a VAR model to examine the linear dependence among the BRICS markets. The results from the VAR model analysis provided some evidence of unidirectional linear dependencies of the Indian and Chinese markets on the Brazilian stock market. The univariate GARCH(1,1) and multivariate GARCH models were employed to explore the volatility and dynamic correlation in the BRICS stock returns respectively. The results of the univariate GARCH model suggested volatility persistence among all the BRICS stock returns where China appeared to be the most volatile followed by the Russian stock market while the South African market was found to be the least volatile. Results from the multivariate GARCH models revealed similar volatility persistence. Furthermore, we found that, the correlations among the five emerging markets varied with time. From this study, evidence of interdependence among the BRICS cannot be rejected. Moreover, it appears that there are other factors apart from the internal markets themselves that may affect the volatility and correlation among the BRICS.