Comparer des méthodes

	Modèle ARIMA (Autoregressive Integrated Moving Average)	Autoregressive Conditional Heteroskedasticity généralisée (GARCH)
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	2015	1986
Auteur d'origine	Box & Jenkins (Box-Jenkins methodology)	Tim Bollerslev
Type	Univariate time-series model	Conditional volatility model
Source fondatrice	Box, G. E. P., Jenkins, G. M., Reinsel, G. C. & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley. ISBN: 978-1118675021	Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307-327.
Alias	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	GARCH(1,1), generalized ARCH, conditional volatility model, GARCH Modeli
Apparentées	5	5
Résumé	ARIMA is a univariate time-series forecasting model that combines autoregressive, integrated (differencing), and moving-average components to predict a single continuous series from its own past. It is the centrepiece of the Box-Jenkins methodology set out in Box, Jenkins, Reinsel & Ljung's Time Series Analysis (5th ed., 2015).	GARCH is an econometric model for the time-varying volatility of financial time series, introduced by Tim Bollerslev in 1986 as a generalisation of Engle's ARCH model. It treats the conditional variance as a function of past squared shocks and past variances, capturing the volatility clustering seen in returns.