Comparer des méthodes

	Modèle TGARCH (Threshold GARCH)	Modèle GARCH (Prévision de la volatilité)
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	1993-1994	1986
Auteur d'origine	Zakoian (1994); Glosten, Jagannathan & Runkle (1993)	Tim Bollerslev
Type	Asymmetric volatility model	Conditional volatility model
Source fondatrice	Zakoian, J.-M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931-955.	Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307–327.
Alias	Threshold GARCH, TGARCH, GJR-GARCH, asymmetric GARCH	GARCH, GARCH(1,1), conditional volatility model, GARCH Modeli (Oynaklık Tahmini)
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 Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model, introduced by Tim Bollerslev in 1986, models the time-varying conditional variance of a financial time series. It captures volatility clustering and the ARCH effect, and is the standard tool for estimating risk and volatility in return series.