Compară metode

	Modelul DCC-GARCH (Corelație Condițională Dinamică)	Modelul ARCH (Autoregresiv Conditional Eteroskedastic)
Domeniu	Econometrie	Econometrie
Familie	Regression model	Regression model
Anul apariției	2002	1982
Autorul original	Robert F. Engle	Robert F. Engle
Tip	Multivariate volatility model	Conditional volatility model
Sursa seminală	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.	Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007.
Denumiri alternative	DCC-GARCH, Dynamic Conditional Correlation GARCH, Engle DCC model, multivariate DCC	ARCH, autoregressive conditional heteroskedasticity, Engle ARCH, conditional variance model
Înrudite	5	6
Rezumat	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.	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.