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Multivariate t- distribution and garch modelling of volatility and conditional correlations on Brics stock markets

We examine the nature of BRICS stock market returns using a t-DCC model and investigate whether multivariate volatility models can characterize and quantify market risk. We initially consider a multivariate normal-DCC model and show that it cannot adequately capture the fat tails prevalent in financial time series data. We then consider a multivariate t- version of the Gaussian dynamic conditional correlation (DCC) proposed by [16] and successfully implemented by [24, 26]. We find that the t-DCC model (dynamic conditional correlation based on the t-distribution) out performs the normal-DCC model. The former passes most diagnostic tests although it barely passes the Kolmogorov-Smirnov goodnessof-fit test. | Multivariate t- distribution and garch modelling of volatility and conditional correlations on Brics stock markets