Comparer des méthodes

	Modèle TGARCH (Threshold GARCH)	Modèle ARCH (Hétéroscédasticité Conditionnelle Autorégressive)
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	1993-1994	1982
Auteur d'origine	Zakoian (1994); Glosten, Jagannathan & Runkle (1993)	Robert F. Engle
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.	Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007.
Alias	Threshold GARCH, TGARCH, GJR-GARCH, asymmetric GARCH	ARCH, autoregressive conditional heteroskedasticity, Engle ARCH, conditional variance model
Apparentées	6	6
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 ARCH model, introduced by Robert Engle in 1982, captures time-varying volatility in financial and macroeconomic time series. It models the conditional variance of today's error as a function of past squared errors, explaining why volatile periods cluster together — a phenomenon known as volatility clustering.