Salīdzināt metodes

	Generalizētā autoregresīvā nosacītā heteroskedastiskuma (GARCH) modelis	DCC-GARCH (dinamiskā nosacītā korelācija)	EGARCH (Exponential GARCH)
Nozare	Ekonometrija	Finanses	Ekonometrija
Saime	Regression model	Regression model	Regression model
Izcelsmes gads	1986	2002	1991
Autors	Tim Bollerslev	Robert F. Engle	Nelson
Tips	Conditional volatility model	Multivariate volatility model	Conditional volatility model (asymmetric GARCH variant)
Pirmavots	Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307-327.	Engle, R. (2002). Dynamic Conditional Correlation: A Simple Class of Multivariate GARCH Models. Journal of Business & Economic Statistics, 20(3), 339-350.	Nelson, D. B. (1991). Conditional Heteroskedasticity in Asset Returns: A New Approach. Econometrica, 59(2), 347-370.
Citi nosaukumi	GARCH(1,1), generalized ARCH, conditional volatility model, GARCH Modeli	dynamic conditional correlation, Engle DCC, multivariate GARCH, DCC-GARCH — Dinamik Koşullu Korelasyon	exponential GARCH, Nelson's EGARCH, asymmetric GARCH, EGARCH — Üstel GARCH
Saistītās	5	5	4
Kopsavilkums	GARCH is an econometric model for the time-varying volatility of financial time series, introduced by Tim Bollerslev in 1986 as a generalisation of Engle's ARCH model. It treats the conditional variance as a function of past squared shocks and past variances, capturing the volatility clustering seen in returns.	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.	EGARCH is an asymmetric GARCH variant, introduced by Nelson in 1991, that models the leverage effect in which bad news raises volatility more than good news of the same size. It captures the negative-shock asymmetry of financial return series by modelling the logarithm of the conditional variance.