Comparer des méthodes

	Modèle DCC-GARCH (Corrélation Conditionnelle Dynamique)	Test de causalité de Granger
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	2002	1969
Auteur d'origine	Robert F. Engle	Clive W. J. Granger
Type	Multivariate volatility model	Causality test (F-test on VAR)
Source fondatrice	Engle, R. F. (2002). Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business and Economic Statistics, 20(3), 339-350.	Granger, C. W. J. (1969). Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica, 37(3), 424–438.
Alias	DCC-GARCH, Dynamic Conditional Correlation GARCH, Engle DCC model, multivariate DCC	Granger test, GC test, predictive causality test, Granger non-causality test
Apparentées	5	5
Résumé	The DCC-GARCH model, introduced by Engle (2002), extends univariate GARCH to capture time-varying correlations between multiple financial time series. It decomposes the multivariate conditional covariance matrix into individual volatility processes and a dynamic correlation matrix, allowing correlations to fluctuate over time while remaining computationally tractable even with many series.	The Granger causality test is a statistical hypothesis test that determines whether past values of one time series help predict future values of another, beyond what that series' own past already explains. Introduced by Clive Granger in 1969, it is the standard approach for assessing predictive causality in VAR-based time-series analysis.