Comparer des méthodes

	Modèle ARIMA (Modèle Autorégressif Intégré à Moyenne Mobile)	Modèle EGARCH (GARCH exponentiel)
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	1970	1991
Auteur d'origine	George Box and Gwilym Jenkins	Daniel B. Nelson
Type	Time series forecasting model	Volatility / conditional variance model
Source fondatrice	Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day.	Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370.
Alias	ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q)	Exponential GARCH, EGARCH, Nelson EGARCH, log-GARCH
Apparentées	6	6
Résumé	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.	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.