Multivariate t- distribution and GARCH modelling of Volatility and Conditional Correlations on BRICS Stock Markets

Smile Dube

Journal of Applied Finance & Banking, 2016, vol. 6, issue 2, 4

Abstract: 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 goodness-of-fit test.

Date: 2016
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