Comparer des méthodes

	Modèle ARCH (Hétéroscédasticité Conditionnelle Autorégressive)	Modèle ARIMA (Autoregressive Integrated Moving Average)
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	1982	2015
Auteur d'origine	Robert F. Engle	Box & Jenkins (Box-Jenkins methodology)
Type	Conditional volatility model	Univariate time-series model
Source fondatrice	Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007.	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
Alias	ARCH, autoregressive conditional heteroskedasticity, Engle ARCH, conditional variance model	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli
Apparentées	6	5
Résumé	The ARCH model, introduced by Robert Engle in 1982, captures time-varying volatility in financial and macroeconomic time series. It models the conditional variance of today's error as a function of past squared errors, explaining why volatile periods cluster together — a phenomenon known as volatility clustering.	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).