The paper develops two Dynamic Conditional Correlation (DCC) models, namely the
Wishart DCC (WDCC) model and the Matrix-Exponential Conditional Correlation
(MECC) model. The paper applies the WDCC approach to the exponential GARCH
(EGARCH) and GJR models to propose asymmetric DCC models. We use the
standardized multivariate t-distribution to accommodate heavy-tailed errors. The paper
presents an empirical example using the trivariate data of the Nikkei 225, Hang Seng
and Straits Times Indices for estimating and forecasting the WDCC-EGARCH and
WDCC-GJR models, and compares the performance with the asymmetric BEKK model.
The empirical results show that AIC and BIC favour the WDCC-EGARCH model to the
WDCC-GJR and asymmetric BEKK models. Moreover, the empirical results indicate
that the WDCC-EGARCH-t model produces reasonable VaR threshold forecasts, which
are very close to the nominal 1% to 3% values.
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