Comparer des méthodes

	Modèle EGARCH Robuste	Modèle TGARCH (Threshold GARCH)
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	2008	1993-1994
Auteur d'origine	Nelson (1991) for EGARCH; robust adaptation via Muler & Yohai (2008) and related authors	Zakoian (1994); Glosten, Jagannathan & Runkle (1993)
Type	Robust volatility model	Asymmetric volatility model
Source fondatrice	Muler, N., & Yohai, V. J. (2008). Robust estimates for GARCH models. Journal of Statistical Planning and Inference, 138(10), 2918–2940.	Zakoian, J.-M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931-955.
Alias	Robust EGARCH model, outlier-robust EGARCH, robust exponential GARCH, REGARCH	Threshold GARCH, TGARCH, GJR-GARCH, asymmetric GARCH
Apparentées	6	6
Résumé	Robust EGARCH extends Nelson's (1991) Exponential GARCH model by replacing standard quasi-maximum likelihood estimation with outlier-resistant procedures — typically bounded-influence or M-estimation — so that a small fraction of extreme observations or data errors cannot distort the estimated volatility dynamics or the leverage effect.	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.