Comparer des méthodes

	Modèle EGARCH (GARCH exponentiel)	Modèle DCC-GARCH (Corrélation Conditionnelle Dynamique)
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	1991	2002
Auteur d'origine	Daniel B. Nelson	Robert F. Engle
Type	Volatility / conditional variance model	Multivariate volatility model
Source fondatrice	Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370.	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.
Alias	Exponential GARCH, EGARCH, Nelson EGARCH, log-GARCH	DCC-GARCH, Dynamic Conditional Correlation GARCH, Engle DCC model, multivariate DCC
Apparentées	6	5
Résumé	The Exponential GARCH (EGARCH) model, introduced by Nelson (1991), extends the standard GARCH framework by modelling the logarithm of conditional variance. This ensures variance is always positive without parameter constraints and, crucially, allows negative and positive shocks to have asymmetric effects on volatility — capturing the well-known leverage effect in financial markets.	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.