examine the nature of BRICS currency 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
such as exchange rates. We then consider a multivariate t- version of the Gaussian dynamic conditional correlation (DCC)
proposed by  and successfully implemented by  and . 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 goodness-of-fit test.
classification numbers: C51, G10, G11
and Volatilities; MGARCH (Multivariate General Autoregressive Conditional Heteroscedasticity),
Multivariate t (t-DCC),
Kolmogorov-Smirnov test, Value at Risk (VaR)
diagnostics, ML – Maximum Likelihood