Сравнение методов

	Робастный TGARCH	Модель EGARCH (Экспоненциальная GARCH)
Область	Эконометрика	Эконометрика
Семейство	Regression model	Regression model
Год появления	1994–2000s	1991
Автор метода	Zakoian (1994) for TGARCH; robust extensions developed through quasi-maximum likelihood and M-estimation literature	Daniel B. Nelson
Тип	Volatility model with asymmetry and robust estimation	Volatility / conditional variance model
Основополагающий источник	Zakoian, J.-M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931–955.	Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370.
Другие названия	robust GJR-GARCH, robust threshold GARCH, heavy-tail TGARCH, outlier-robust TGARCH	Exponential GARCH, EGARCH, Nelson EGARCH, log-GARCH
Связанные	6	6
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 Exponential GARCH (EGARCH) model, introduced by Nelson (1991), extends the standard GARCH framework by modelling the logarithm of conditional variance. This ensures variance is always positive without parameter constraints and, crucially, allows negative and positive shocks to have asymmetric effects on volatility — capturing the well-known leverage effect in financial markets.