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Dynamic Conditional Correlations for Asymmetric Processes
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Dynamic Conditional Correlations for Asymmetric Processes

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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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  • Manabu Asai & Michael McAleer, 2010. "Dynamic Conditional Correlations for Asymmetric Processes," Working Papers in Economics 10/76, University of Canterbury, Department of Economics and Finance.
    • Handle: RePEc:cbt:econwp:10/76
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    Keywords

    Dynamic conditional correlations; Matrix exponential model; Wishart process; EGARCH; GJR; asymmetric BEKK; heavy-tailed errors;
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