文章基本信息

标题：Dynamic Conditional Correlations for Asymmetric Processes
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作者：Manabu Asai ; Michael McAleer
期刊名称：JOURNAL OF THE JAPAN STATISTICAL SOCIETY
印刷版ISSN：1882-2754
电子版ISSN：1348-6365
出版年度：2011
卷号：41
期号：2
页码：143-157
DOI：10.14490/jjss.41.143
出版社：JAPAN STATISTICAL SOCIETY
摘要：The paper develops a new Dynamic Conditional Correlation (DCC) model, namely the Wishart DCC (wDCC) 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, asymmetric BEKK and alternative conventional DCC 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.
关键词：Asymmetric BEKK;dynamic conditional correlations;EGARCH;GJR;heavy-tailed errors;Wishart process