Сравнение на методи

	Robust GARCH модел	Модел GARCH (Прогнозиране на волатилността)
Област	Иконометрия	Иконометрия
Семейство	Regression model	Regression model
Година на възникване	1986–2013	1986
Създател	Boudt, Danielsson & Laurent (robust extensions); Bollerslev (standard GARCH, 1986)	Tim Bollerslev
Тип	Volatility model	Conditional volatility model
Основополагащ източник	Boudt, K., Danielsson, J., & Laurent, S. (2013). Robust forecasting of dynamic conditional correlation GARCH models. International Journal of Forecasting, 29(2), 244–257.	Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307–327.
Други названия	Robust GARCH, outlier-robust GARCH, heavy-tail GARCH, contamination-robust volatility model	GARCH, GARCH(1,1), conditional volatility model, GARCH Modeli (Oynaklık Tahmini)
Свързани	5	5
Резюме	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 Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model, introduced by Tim Bollerslev in 1986, models the time-varying conditional variance of a financial time series. It captures volatility clustering and the ARCH effect, and is the standard tool for estimating risk and volatility in return series.