Comparer des méthodes

	TGARCH Robuste	Modèle TGARCH (Threshold GARCH)
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	1994–2000s	1993-1994
Auteur d'origine	Zakoian (1994) for TGARCH; robust extensions developed through quasi-maximum likelihood and M-estimation literature	Zakoian (1994); Glosten, Jagannathan & Runkle (1993)
Type	Volatility model with asymmetry and robust estimation	Asymmetric volatility model
Source fondatrice	Zakoian, J.-M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931–955.	Zakoian, J.-M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931-955.
Alias	robust GJR-GARCH, robust threshold GARCH, heavy-tail TGARCH, outlier-robust TGARCH	Threshold GARCH, TGARCH, GJR-GARCH, asymmetric GARCH
Apparentées	6	6
Résumé	Robust TGARCH extends the Threshold GARCH model by replacing the conventional maximum likelihood objective with an estimator that is resistant to heavy-tailed innovations and outlying observations. It captures asymmetric volatility responses — where negative shocks amplify variance more than positive shocks — while remaining reliable when the return distribution deviates strongly from normality.	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.