Comparer des méthodes

	Modèle GARCH Robuste	Régression quantile
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	1986–2013	1978
Auteur d'origine	Boudt, Danielsson & Laurent (robust extensions); Bollerslev (standard GARCH, 1986)	Koenker & Bassett
Type	Volatility model	Conditional quantile regression
Source fondatrice	Boudt, K., Danielsson, J., & Laurent, S. (2013). Robust forecasting of dynamic conditional correlation GARCH models. International Journal of Forecasting, 29(2), 244–257.	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.
Alias	Robust GARCH, outlier-robust GARCH, heavy-tail GARCH, contamination-robust volatility model	conditional quantile regression, regression quantiles, Kantil Regresyon
Apparentées	5	5
Résumé	The Robust GARCH model extends the classical GARCH framework to handle outliers and heavy-tailed innovations that commonly appear in financial return series. By down-weighting extreme observations through a robust innovation term, it produces more reliable volatility forecasts when data contain jumps, crises, or other anomalies that would otherwise distort standard GARCH estimates.	Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails.