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.

EGARCH, GJR, asymmetric BEKK, dynamic conditional correlations, heavy-tailed errors, matrix exponential model, wishart process
Erasmus School of Economics
hdl.handle.net/1765/21949
Econometric Institute Research Papers
Report / Econometric Institute, Erasmus University Rotterdam
Erasmus School of Economics

Asai, M, & McAleer, M.J. (2010). Dynamic Conditional Correlations for Asymmetric Processes (No. EI 2010-76). Report / Econometric Institute, Erasmus University Rotterdam (pp. 1–25). Erasmus School of Economics. Retrieved from http://hdl.handle.net/1765/21949