Сравнение методов

	Робастная модель GARCH	Модель EGARCH (Экспоненциальная GARCH)
Область	Эконометрика	Эконометрика
Семейство	Regression model	Regression model
Год появления	1986–2013	1991
Автор метода	Boudt, Danielsson & Laurent (robust extensions); Bollerslev (standard GARCH, 1986)	Daniel B. Nelson
Тип	Volatility model	Volatility / conditional variance model
Основополагающий источник	Boudt, K., Danielsson, J., & Laurent, S. (2013). Robust forecasting of dynamic conditional correlation GARCH models. International Journal of Forecasting, 29(2), 244–257.	Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370.
Другие названия	Robust GARCH, outlier-robust GARCH, heavy-tail GARCH, contamination-robust volatility model	Exponential GARCH, EGARCH, Nelson EGARCH, log-GARCH
Связанные	5	6
Сводка	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.	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.