TY  - JOUR
TT  - Estimation of Volatility and Correlation with Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models: An Application to Moroccan Stock Markets
AU  - Belasri, Yassine
AU  - Ellaia, Rachid
PY  - 2017
DA  - June
JF  - International Journal of Economics and Financial Issues
JO  - IJEFI
PB  - İlhan ÖZTÜRK
WT  - DergiPark
SN  - 2146-4138
SP  - 384
EP  - 396
VL  - 7
IS  - 2
KW  - Volatility
KW  - Correlation
KW  - Multivariate Generalized Autoregressive Conditional Heteroskedasticity
KW  - Diagonal Baba
KW  - Engle
KW  - Kraft and Kroner
KW  - Dynamic Conditional Correlation
KW  - Stock Markets
KW  - Morocco
N2  - 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.
UR  - https://dergipark.org.tr/tr/pub/ijefi/article/354502
L1  - https://dergipark.org.tr/tr/download/article-file/365811
ER  -