Comparer des méthodes

	Modèle ARCH Robuste	Modèle ARCH (Hétéroscédasticité Conditionnelle Autorégressive)	Modèle EGARCH (GARCH exponentiel)
Domaine	Économétrie	Économétrie	Économétrie
Famille	Regression model	Regression model	Regression model
Année d'origine	2002–2008	1982	1991
Auteur d'origine	Engle (1982) for ARCH; robust variants developed by Muler, Yohai, and others from the early 2000s	Robert F. Engle	Daniel B. Nelson
Type	Volatility / conditional heteroscedasticity model	Conditional volatility model	Volatility / conditional variance model
Source fondatrice	Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007.	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	robust ARCH, outlier-robust ARCH, heavy-tailed ARCH, robust conditional volatility model	ARCH, autoregressive conditional heteroskedasticity, Engle ARCH, conditional variance model	Exponential GARCH, EGARCH, Nelson EGARCH, log-GARCH
Apparentées	6	6	6
Résumé	The Robust ARCH model extends the classical Autoregressive Conditional Heteroscedasticity framework by replacing the standard maximum-likelihood estimator with robust alternatives that downweight or eliminate the influence of outliers. This makes volatility estimates resistant to extreme observations that frequently contaminate financial and macroeconomic time series.	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.