Salīdzināt metodes

	DCC-GARCH modelis (Dynamic Conditional Correlation)	Vektora autoregresija (VAR)
Nozare	Ekonometrija	Ekonometrija
Saime	Regression model	Regression model
Izcelsmes gads	2002	1980
Autors	Robert F. Engle	Christopher A. Sims
Tips	Multivariate volatility model	Multivariate time-series model
Pirmavots	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.
Citi nosaukumi	DCC-GARCH, Dynamic Conditional Correlation GARCH, Engle DCC model, multivariate DCC	VAR, VAR model, vector autoregressive model, multivariate autoregression
Saistītās	5	5
Kopsavilkums	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.