Estimation of Volatility and Correlation with Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models: An Application to Moroccan Stock Markets

Yassine Belasri and Rachid Ellaia
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Yassine Belasri: Laboratory of Study and Research in Applied Mathematics, LERMA, Mohammed V University, Agdal, Morocco,
Rachid Ellaia: Laboratory of Study and Research in Applied Mathematics, LERMA, Mohammed V University, Agdal, Morocco,

International Journal of Economics and Financial Issues, 2017, vol. 7, issue 2, 384-396

Abstract: Volatility and correlation are important metrics of risk evaluation for financial markets worldwide. The latter have shown that these tools are varying over time, thus, they require an appropriate estimation models to adequately capture their dynamics. Multivariate generalized autoregressive conditional heteroskedasticity (GARCH) models were developed for this purpose and have known a great success. The purpose of this article is to examine the performance of multivariate GARCH models to estimate variance covariance matrices in application to 10 years of daily stock prices in Moroccan stock markets. The estimation is done through the most widely used multivariate GARCH models, dynamic conditional correlation (DCC) and Baba, Engle, Kraft and Kroner (BEKK) models. A comparison of estimated results is done using multiple statistical tests and with application to volatility forecast and value at risk (VaR) calculation. The results show that BEKK model performs better than DCC in modeling variance covariance matrices and that both models failed to adequately estimate VaR.

Keywords: Volatility, Correlation, Multivariate Generalized Autoregressive Conditional Heteroskedasticity, Diagonal Baba; Engle; Kraft and Kroner, Dynamic Conditional Correlation, Stock Markets, Morocco (search for similar items in EconPapers)
JEL-codes: C3 E44 G1 (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (1)

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