Porównaj metody

	Model Robust EGARCH	Solidny TGARCH
Dziedzina	Ekonometria	Ekonometria
Rodzina	Regression model	Regression model
Rok powstania	2008	1994–2000s
Twórca	Nelson (1991) for EGARCH; robust adaptation via Muler & Yohai (2008) and related authors	Zakoian (1994) for TGARCH; robust extensions developed through quasi-maximum likelihood and M-estimation literature
Typ	Robust volatility model	Volatility model with asymmetry and robust estimation
Źródło pierwotne	Muler, N., & Yohai, V. J. (2008). Robust estimates for GARCH models. Journal of Statistical Planning and Inference, 138(10), 2918–2940.	Zakoian, J.-M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931–955.
Inne nazwy	Robust EGARCH model, outlier-robust EGARCH, robust exponential GARCH, REGARCH	robust GJR-GARCH, robust threshold GARCH, heavy-tail TGARCH, outlier-robust TGARCH
Pokrewne	6	6
Podsumowanie	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.	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.