Porównaj metody

	Model Robust GARCH	Model ARCH (Autoregresywna Heteroskedastyczność Warunkowa)	Model GARCH (Prognozowanie zmienności)
Dziedzina	Ekonometria	Ekonometria	Ekonometria
Rodzina	Regression model	Regression model	Regression model
Rok powstania	1986–2013	1982	1986
Twórca	Boudt, Danielsson & Laurent (robust extensions); Bollerslev (standard GARCH, 1986)	Robert F. Engle	Tim Bollerslev
Typ	Volatility model	Conditional volatility model	Conditional volatility model
Źródło pierwotne	Boudt, K., Danielsson, J., & Laurent, S. (2013). Robust forecasting of dynamic conditional correlation GARCH models. International Journal of Forecasting, 29(2), 244–257.	Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007.	Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307–327.
Inne nazwy	Robust GARCH, outlier-robust GARCH, heavy-tail GARCH, contamination-robust volatility model	ARCH, autoregressive conditional heteroskedasticity, Engle ARCH, conditional variance model	GARCH, GARCH(1,1), conditional volatility model, GARCH Modeli (Oynaklık Tahmini)
Pokrewne	5	6	5
Podsumowanie	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 ARCH model, introduced by Robert Engle in 1982, captures time-varying volatility in financial and macroeconomic time series. It models the conditional variance of today's error as a function of past squared errors, explaining why volatile periods cluster together — a phenomenon known as volatility clustering.	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.