Comparer des méthodes

	Modèle GARCH non linéaire	Modèle EGARCH (GARCH exponentiel)
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	1991-1993	1991
Auteur d'origine	Glosten, Jagannathan & Runkle; Nelson (1991) for EGARCH	Daniel B. Nelson
Type	Volatility model	Volatility / conditional variance model
Source fondatrice	Glosten, L. R., Jagannathan, R., & Runkle, D. E. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. Journal of Finance, 48(5), 1779-1801.	Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370.
Alias	NL-GARCH, asymmetric GARCH, GJR-GARCH, nonlinear volatility model	Exponential GARCH, EGARCH, Nelson EGARCH, log-GARCH
Apparentées	6	6
Résumé	The Nonlinear GARCH model extends the standard GARCH framework to capture asymmetric and nonlinear responses of conditional volatility to past shocks. It allows negative returns (bad news) to amplify volatility more than positive returns of equal magnitude, a phenomenon known as the leverage effect, which is empirically pervasive in financial markets.	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.