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

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 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 ten 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 calculation. The results show that BEKK model performs better than DCC in modeling variance covariance matrices and that both models failed to adequately estimate Value at Risk.

Keywords

• volatility
• correlation
• multivariate generalized autoregressive conditional heteroskedasticity
• diagonal baba
• engle
• kraft and kroner
• dynamic conditional correlation
• stock markets
• morocco