Confronta i metodi

	Modello ARCH Robusto	Modello EGARCH (Exponential GARCH)	Regressione quantilica
Campo	Econometria	Econometria	Econometria
Famiglia	Regression model	Regression model	Regression model
Anno di origine	2002–2008	1991	1978
Ideatore	Engle (1982) for ARCH; robust variants developed by Muler, Yohai, and others from the early 2000s	Daniel B. Nelson	Koenker & Bassett
Tipo	Volatility / conditional heteroscedasticity model	Volatility / conditional variance model	Conditional quantile regression
Fonte seminale	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.	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.
Alias	robust ARCH, outlier-robust ARCH, heavy-tailed ARCH, robust conditional volatility model	Exponential GARCH, EGARCH, Nelson EGARCH, log-GARCH	conditional quantile regression, regression quantiles, Kantil Regresyon
Correlati	6	6	5
Sintesi	The Robust ARCH model extends the classical Autoregressive Conditional Heteroscedasticity framework by replacing the standard maximum-likelihood estimator with robust alternatives that downweight or eliminate the influence of outliers. This makes volatility estimates resistant to extreme observations that frequently contaminate financial and macroeconomic time series.	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.