Võrdle meetodeid

	DCC-GARCH (dünaamiline tingimuslik korrelatsioon)	GARCH-mudel (volatiilsuse prognoosimine)	Vektorautoregressiooni (VAR) mudel
Valdkond	Rahandus	Ökonomeetria	Ökonomeetria
Perekond	Regression model	Regression model	Regression model
Tekkeaasta	2002	1986	2005
Looja	Robert F. Engle	Tim Bollerslev	Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition
Tüüp	Multivariate volatility model	Conditional volatility model	Multivariate time-series model
Algallikas	Engle, R. (2002). Dynamic Conditional Correlation: A Simple Class of Multivariate GARCH Models. Journal of Business & Economic Statistics, 20(3), 339-350.	Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307–327.	Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer.
Rööpnimetused	dynamic conditional correlation, Engle DCC, multivariate GARCH, DCC-GARCH — Dinamik Koşullu Korelasyon	GARCH, GARCH(1,1), conditional volatility model, GARCH Modeli (Oynaklık Tahmini)	vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon
Seotud	5	5	4
Kokkuvõte	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.	The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model, introduced by Tim Bollerslev in 1986, models the time-varying conditional variance of a financial time series. It captures volatility clustering and the ARCH effect, and is the standard tool for estimating risk and volatility in return series.	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).