Comparer des méthodes

	Modèle DCC-GARCH (Corrélation Conditionnelle Dynamique)	Autoregressive Vectoriel (VAR)
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	2002	1980
Auteur d'origine	Robert F. Engle	Christopher A. Sims
Type	Multivariate volatility model	Multivariate time-series model
Source fondatrice	Engle, R. F. (2002). Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business and Economic Statistics, 20(3), 339-350.	Sims, C. A. (1980). Macroeconomics and Reality. Econometrica, 48(1), 1–48.
Alias	DCC-GARCH, Dynamic Conditional Correlation GARCH, Engle DCC model, multivariate DCC	VAR, VAR model, vector autoregressive model, multivariate autoregression
Apparentées	5	5
Résumé	The DCC-GARCH model, introduced by Engle (2002), extends univariate GARCH to capture time-varying correlations between multiple financial time series. It decomposes the multivariate conditional covariance matrix into individual volatility processes and a dynamic correlation matrix, allowing correlations to fluctuate over time while remaining computationally tractable even with many series.	Vector Autoregression is a multivariate time-series model in which each variable is regressed on its own lags and the lags of all other variables in the system. Originally proposed by Sims (1980) as a data-driven alternative to large structural macroeconomic models, VAR has become the standard workhorse for dynamic analysis in empirical economics and finance.