Comparer des méthodes

	Modèle DCC-GARCH robuste (DCC-GARCH robuste)	Modèle EGARCH Robuste
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	2002–2021	2008
Auteur d'origine	Engle (2002) for DCC; robust extensions by Pakel, Shephard, Sheppard, and Engle (2021)	Nelson (1991) for EGARCH; robust adaptation via Muler & Yohai (2008) and related authors
Type	Multivariate volatility model with robust estimation	Robust volatility model
Source fondatrice	Engle, R. F. (2002). Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business and Economic Statistics, 20(3), 339–350.	Muler, N., & Yohai, V. J. (2008). Robust estimates for GARCH models. Journal of Statistical Planning and Inference, 138(10), 2918–2940.
Alias	robust DCC-GARCH, robust dynamic conditional correlation, outlier-robust DCC, composite-likelihood DCC-GARCH	Robust EGARCH model, outlier-robust EGARCH, robust exponential GARCH, REGARCH
Apparentées	6	6
Résumé	The Robust DCC-GARCH model extends Engle's (2002) Dynamic Conditional Correlation framework by replacing standard quasi-maximum likelihood estimation with outlier-resistant or composite-likelihood techniques. This preserves accurate time-varying correlation estimation even when financial return data contain extreme observations, heavy tails, or structural irregularities.	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.