Comparer des méthodes

	Modèle TGARCH (Threshold GARCH)	Modèle DCC-GARCH (Corrélation Conditionnelle Dynamique)
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	1993-1994	2002
Auteur d'origine	Zakoian (1994); Glosten, Jagannathan & Runkle (1993)	Robert F. Engle
Type	Asymmetric volatility model	Multivariate volatility model
Source fondatrice	Zakoian, J.-M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931-955.	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	Threshold GARCH, TGARCH, GJR-GARCH, asymmetric GARCH	DCC-GARCH, Dynamic Conditional Correlation GARCH, Engle DCC model, multivariate DCC
Apparentées	6	5
Résumé	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.	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.