Comparer des méthodes

	Modèle TGARCH (Threshold GARCH)	Modèle ARCH (Hétéroscédasticité Conditionnelle Autorégressive)	Modèle EGARCH (GARCH exponentiel)
Domaine	Économétrie	Économétrie	Économétrie
Famille	Regression model	Regression model	Regression model
Année d'origine	1993-1994	1982	1991
Auteur d'origine	Zakoian (1994); Glosten, Jagannathan & Runkle (1993)	Robert F. Engle	Daniel B. Nelson
Type	Asymmetric volatility model	Conditional volatility model	Volatility / conditional variance 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.	Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370.
Alias	Threshold GARCH, TGARCH, GJR-GARCH, asymmetric GARCH	ARCH, autoregressive conditional heteroskedasticity, Engle ARCH, conditional variance model	Exponential GARCH, EGARCH, Nelson EGARCH, log-GARCH
Apparentées	6	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.	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.