Confronta i metodi

	Modello GARCH Robusto	Modello EGARCH (Exponential GARCH)	Regressione quantilica
Campo	Econometria	Econometria	Econometria
Famiglia	Regression model	Regression model	Regression model
Anno di origine	1986–2013	1991	1978
Ideatore	Boudt, Danielsson & Laurent (robust extensions); Bollerslev (standard GARCH, 1986)	Daniel B. Nelson	Koenker & Bassett
Tipo	Volatility model	Volatility / conditional variance model	Conditional quantile regression
Fonte seminale	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.	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.
Alias	Robust GARCH, outlier-robust GARCH, heavy-tail GARCH, contamination-robust volatility model	Exponential GARCH, EGARCH, Nelson EGARCH, log-GARCH	conditional quantile regression, regression quantiles, Kantil Regresyon
Correlati	5	6	5
Sintesi	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.	Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails.