Comparer des méthodes

	Modèle DCC-GARCH (Corrélation Conditionnelle Dynamique)	Modèle TGARCH (Threshold GARCH)
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	2002	1993-1994
Auteur d'origine	Robert F. Engle	Zakoian (1994); Glosten, Jagannathan & Runkle (1993)
Type	Multivariate volatility model	Asymmetric volatility model
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.	Zakoian, J.-M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931-955.
Alias	DCC-GARCH, Dynamic Conditional Correlation GARCH, Engle DCC model, multivariate DCC	Threshold GARCH, TGARCH, GJR-GARCH, asymmetric GARCH
Apparentées	5	6
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 Threshold GARCH (TGARCH) model extends the standard GARCH framework by allowing positive and negative return shocks to have asymmetric effects on conditional variance. Negative shocks — bad news — typically amplify volatility more than positive shocks of the same magnitude, a stylised fact known as the leverage effect. TGARCH captures this asymmetry through a threshold indicator that switches on when the previous period's shock was negative.