Σύγκριση μεθόδων

	Στιβαρό Μοντέλο GARCH	Μοντέλο ARCH (Αυτοπαλίνδρομη Συνθηκική Ετεροσκεδαστικότητα)	Μοντέλο EGARCH (Exponential GARCH)
Πεδίο	Οικονομετρία	Οικονομετρία	Οικονομετρία
Οικογένεια	Regression model	Regression model	Regression model
Έτος προέλευσης	1986–2013	1982	1991
Δημιουργός	Boudt, Danielsson & Laurent (robust extensions); Bollerslev (standard GARCH, 1986)	Robert F. Engle	Daniel B. Nelson
Τύπος	Volatility model	Conditional volatility model	Volatility / conditional variance model
Θεμελιώδης πηγή	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.	Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370.
Εναλλακτικές ονομασίες	Robust GARCH, outlier-robust GARCH, heavy-tail GARCH, contamination-robust volatility model	ARCH, autoregressive conditional heteroskedasticity, Engle ARCH, conditional variance model	Exponential GARCH, EGARCH, Nelson EGARCH, log-GARCH
Συναφείς	5	6	6
Σύνοψη	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 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.