Salīdzināt metodes

	BEKK-GARCH: Daudzdimensiju nosacītās mainīguma modelēšana	GARCH modelis (volatilitātes prognozēšana)	Vektora autoregresijas (VAR) modelis
Nozare	Ekonometrija	Ekonometrija	Ekonometrija
Saime	Regression model	Regression model	Regression model
Izcelsmes gads	1995	1986	2005
Autors	Robert Engle & Kenneth Kroner	Tim Bollerslev	Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition
Tips	Multivariate conditional volatility model	Conditional volatility model	Multivariate time-series model
Pirmavots	Engle, R. F., & Kroner, K. F. (1995). Multivariate simultaneous generalized ARCH. Econometric Theory, 11(1), 122–150.	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.
Citi nosaukumi	BEKK Model, Baba-Engle-Kraft-Kroner GARCH, Multivariate BEKK, BEKK-ÇARCH Modeli	GARCH, GARCH(1,1), conditional volatility model, GARCH Modeli (Oynaklık Tahmini)	vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon
Saistītās	3	5	4
Kopsavilkums	BEKK-GARCH, proposed by Engle and Kroner (1995), is a multivariate GARCH specification that models the time-varying conditional covariance matrix of a system of financial return series. Named after Baba, Engle, Kraft, and Kroner, it is the dominant framework for quantifying volatility spillovers and dynamic correlations across multiple assets or markets simultaneously, widely adopted by financial economists and risk managers since the mid-1990s.	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).