Comparer des méthodes

	DCC-GARCH (Dynamic Conditional Correlation)	Modèle de Vector Autoregression (VAR)
Domaine	Finance	Économétrie
Famille	Regression model	Regression model
Année d'origine	2002	2005
Auteur d'origine	Robert F. Engle	Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition
Type	Multivariate volatility model	Multivariate time-series model
Source fondatrice	Engle, R. (2002). Dynamic Conditional Correlation: A Simple Class of Multivariate GARCH Models. Journal of Business & Economic Statistics, 20(3), 339-350.	Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer.
Alias	dynamic conditional correlation, Engle DCC, multivariate GARCH, DCC-GARCH — Dinamik Koşullu Korelasyon	vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon
Apparentées	5	4
Résumé	DCC-GARCH is Engle's (2002) multivariate volatility model that lets the correlations between several assets change over time. A separate univariate GARCH model is fitted to each series, and then the dynamic correlation matrix is estimated in a second, separate step.	Vector Autoregression is a multivariate time-series model that treats several interdependent series symmetrically, letting each variable depend on its own past values and the past values of all the others. It is the standard tool for capturing mutual causality and joint dynamics, developed in the modern multiple-time-series tradition treated by Lütkepohl (2005).