Comparer des méthodes

	Modèle EGARCH (GARCH exponentiel)	Modèle ARIMA (Modèle Autorégressif Intégré à Moyenne Mobile)
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	1991	1970
Auteur d'origine	Daniel B. Nelson	George Box and Gwilym Jenkins
Type	Volatility / conditional variance model	Time series forecasting model
Source fondatrice	Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370.	Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day.
Alias	Exponential GARCH, EGARCH, Nelson EGARCH, log-GARCH	ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q)
Apparentées	6	6
Résumé	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.	The ARIMA(p,d,q) model is the standard workhorse for univariate time series forecasting. It combines autoregressive terms (past values), differencing to induce stationarity, and moving average terms (past shocks) into a unified linear framework. Developed by Box and Jenkins (1970), it remains one of the most widely applied models in econometrics and applied statistics.