Сравнение методов

	Робастный TGARCH	Робастная модель GARCH
Область	Эконометрика	Эконометрика
Семейство	Regression model	Regression model
Год появления	1994–2000s	1986–2013
Автор метода	Zakoian (1994) for TGARCH; robust extensions developed through quasi-maximum likelihood and M-estimation literature	Boudt, Danielsson & Laurent (robust extensions); Bollerslev (standard GARCH, 1986)
Тип	Volatility model with asymmetry and robust estimation	Volatility model
Основополагающий источник	Zakoian, J.-M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931–955.	Boudt, K., Danielsson, J., & Laurent, S. (2013). Robust forecasting of dynamic conditional correlation GARCH models. International Journal of Forecasting, 29(2), 244–257.
Другие названия	robust GJR-GARCH, robust threshold GARCH, heavy-tail TGARCH, outlier-robust TGARCH	Robust GARCH, outlier-robust GARCH, heavy-tail GARCH, contamination-robust volatility model
Связанные	6	5
Сводка	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.	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.