Comparer des méthodes

	Modèle DCC-GARCH robuste (DCC-GARCH robuste)	Modèle GARCH Robuste
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	2002–2021	1986–2013
Auteur d'origine	Engle (2002) for DCC; robust extensions by Pakel, Shephard, Sheppard, and Engle (2021)	Boudt, Danielsson & Laurent (robust extensions); Bollerslev (standard GARCH, 1986)
Type	Multivariate volatility model with robust estimation	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.	Boudt, K., Danielsson, J., & Laurent, S. (2013). Robust forecasting of dynamic conditional correlation GARCH models. International Journal of Forecasting, 29(2), 244–257.
Alias	robust DCC-GARCH, robust dynamic conditional correlation, outlier-robust DCC, composite-likelihood DCC-GARCH	Robust GARCH, outlier-robust GARCH, heavy-tail GARCH, contamination-robust volatility model
Apparentées	6	5
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.	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.