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

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  • Smile Dube

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.

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  • Smile Dube, 2016. "Multivariate t- distribution and GARCH modelling of Volatility and Conditional Correlations on BRICS Stock Markets," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 6(2), pages 1-4.
    • Handle: RePEc:spt:apfiba:v:6:y:2016:i:2:f:6_2_4
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    Cited by:

    1. Li Wang, 2019. "The Risk Spillover Effects of Securities Companies in China’s Capital Market with the CoVaR Method," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 9(3), pages 1-7.

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