Comparer des méthodes

	Modèle ARIMA (Modèle Autorégressif Intégré à Moyenne Mobile)	Modèle GARCH (Prévision de la volatilité)
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	1970	1986
Auteur d'origine	George Box and Gwilym Jenkins	Tim Bollerslev
Type	Time series forecasting model	Conditional volatility model
Source fondatrice	Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day.	Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307–327.
Alias	ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q)	GARCH, GARCH(1,1), conditional volatility model, GARCH Modeli (Oynaklık Tahmini)
Apparentées	6	5
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 Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model, introduced by Tim Bollerslev in 1986, models the time-varying conditional variance of a financial time series. It captures volatility clustering and the ARCH effect, and is the standard tool for estimating risk and volatility in return series.