Comparer des méthodes

	Modèle ARCH Robuste	Régression Robuste
Domaine	Économétrie	Statistique
Famille	Regression model	Regression model
Année d'origine	2002–2008	1964
Auteur d'origine	Engle (1982) for ARCH; robust variants developed by Muler, Yohai, and others from the early 2000s	Peter J. Huber (M-estimation, 1964); Frank Hampel (influence function, 1974)
Type	Volatility / conditional heteroscedasticity model	Regression with outlier resistance
Source fondatrice	Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007.	Huber, P. J. (1964). Robust estimation of a location parameter. The Annals of Mathematical Statistics, 35(1), 73–101.
Alias	robust ARCH, outlier-robust ARCH, heavy-tailed ARCH, robust conditional volatility model	M-estimation regression, robust linear regression, outlier-resistant regression, MM-estimation
Apparentées	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.	Robust regression estimates the linear relationship between a continuous outcome and predictors while sharply reducing the influence of outliers and leverage points. Unlike OLS, which is highly sensitive to extreme observations, robust methods assign down-weighted influence to atypical data points, producing coefficient estimates that remain stable even when a fraction of the data is contaminated or non-normally distributed.