Sammenlign metoder

	DCC-GARCH-modellen (Dynamic Conditional Correlation)	ARCH-modell (Autoregressive Conditional Heteroskedasticity)
Fagfelt	Økonometri	Økonometri
Familie	Regression model	Regression model
Opprinnelsesår	2002	1982
Opphavsperson	Robert F. Engle	Robert F. Engle
Type	Multivariate volatility model	Conditional volatility model
Opprinnelig kilde	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.
Alias	DCC-GARCH, Dynamic Conditional Correlation GARCH, Engle DCC model, multivariate DCC	ARCH, autoregressive conditional heteroskedasticity, Engle ARCH, conditional variance model
Relaterte	5	6
Sammendrag	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.