Comparer des méthodes

	Modèle DCC-GARCH non linéaire (Corrélation dynamique conditionnelle asymétrique)	Modèle EGARCH (GARCH exponentiel)
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	2006	1991
Auteur d'origine	Cappiello, Engle & Sheppard	Daniel B. Nelson
Type	Multivariate volatility and correlation model	Volatility / conditional variance model
Source fondatrice	Cappiello, L., Engle, R. F., & Sheppard, K. (2006). Asymmetric dynamics in the correlations of global equity and bond returns. Journal of Financial Econometrics, 4(4), 537–572.	Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370.
Alias	ADCC-GARCH, Asymmetric DCC-GARCH, NL-DCC-GARCH, Nonlinear Asymmetric DCC	Exponential GARCH, EGARCH, Nelson EGARCH, log-GARCH
Apparentées	2	6
Résumé	The Nonlinear DCC-GARCH model extends Engle's (2002) Dynamic Conditional Correlation framework by allowing correlations to respond asymmetrically to negative versus positive return shocks. Proposed by Cappiello, Engle, and Sheppard (2006), it is the standard tool for measuring time-varying co-movement and contagion effects in multivariate financial time series when bad news is expected to increase correlations more than good news.	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.