Salīdzināt metodes

	Robust EGARCH modelis	Robustais GARCH modelis
Nozare	Ekonometrija	Ekonometrija
Saime	Regression model	Regression model
Izcelsmes gads	2008	1986–2013
Autors	Nelson (1991) for EGARCH; robust adaptation via Muler & Yohai (2008) and related authors	Boudt, Danielsson & Laurent (robust extensions); Bollerslev (standard GARCH, 1986)
Tips	Robust volatility model	Volatility model
Pirmavots	Muler, N., & Yohai, V. J. (2008). Robust estimates for GARCH models. Journal of Statistical Planning and Inference, 138(10), 2918–2940.	Boudt, K., Danielsson, J., & Laurent, S. (2013). Robust forecasting of dynamic conditional correlation GARCH models. International Journal of Forecasting, 29(2), 244–257.
Citi nosaukumi	Robust EGARCH model, outlier-robust EGARCH, robust exponential GARCH, REGARCH	Robust GARCH, outlier-robust GARCH, heavy-tail GARCH, contamination-robust volatility model
Saistītās	6	5
Kopsavilkums	Robust EGARCH extends Nelson's (1991) Exponential GARCH model by replacing standard quasi-maximum likelihood estimation with outlier-resistant procedures — typically bounded-influence or M-estimation — so that a small fraction of extreme observations or data errors cannot distort the estimated volatility dynamics or the leverage effect.	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.