Methoden vergelijken

	Robuuste Dynamische Conditionele Correlatie GARCH (Robuuste DCC-GARCH)	Robuuste TGARCH
Vakgebied	Econometrie	Econometrie
Familie	Regression model	Regression model
Jaar van ontstaan	2002–2021	1994–2000s
Grondlegger	Engle (2002) for DCC; robust extensions by Pakel, Shephard, Sheppard, and Engle (2021)	Zakoian (1994) for TGARCH; robust extensions developed through quasi-maximum likelihood and M-estimation literature
Type	Multivariate volatility model with robust estimation	Volatility model with asymmetry and robust estimation
Oorspronkelijke bron	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.	Zakoian, J.-M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931–955.
Aliassen	robust DCC-GARCH, robust dynamic conditional correlation, outlier-robust DCC, composite-likelihood DCC-GARCH	robust GJR-GARCH, robust threshold GARCH, heavy-tail TGARCH, outlier-robust TGARCH
Verwant	6	6
Samenvatting	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 TGARCH extends the Threshold GARCH model by replacing the conventional maximum likelihood objective with an estimator that is resistant to heavy-tailed innovations and outlying observations. It captures asymmetric volatility responses — where negative shocks amplify variance more than positive shocks — while remaining reliable when the return distribution deviates strongly from normality.