Template-Type: ReDIF-Paper 1.0
Author-Name: Asai, M.
Author-Name-Last: Asai
Author-Name-First: Manabu
Author-Person: pas73
Author-Name: McAleer, M.J.
Author-Name-Last: McAleer
Author-Name-First: Michael
Author-Person: pmc90
Title: Dynamic Conditional Correlations for Asymmetric Processes
Abstract: 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.
Creation-Date: 2010-12-22
File-URL: https://repub.eur.nl/pub/21949/EI2010-76.pdf
File-Format: application/pdf
Series: RePEc:ems:eureir
Number: EI 2010-76
Keywords: EGARCH, GJR, asymmetric BEKK, dynamic conditional correlations, heavy-tailed errors, matrix exponential model, wishart process
Handle: RePEc:ems:eureir:21949