Сравнение на методи

	Обобщена авторегресионна условна хетероскедастичност (GARCH)	DCC-GARCH (Динамична условна корелация)	Експоненциален GARCH (EGARCH)	Просто и двойно експоненциално изглаждане (SES / Holt)
Област	Иконометрия	Финанси	Иконометрия	Иконометрия
Семейство	Regression model	Regression model	Regression model	Regression model
Година на възникване	1986	2002	1991	1957
Създател	Tim Bollerslev	Robert F. Engle	Nelson	Robert G. Brown (SES); Charles C. Holt (linear trend)
Тип	Conditional volatility model	Multivariate volatility model	Conditional volatility model (asymmetric GARCH variant)	Exponential smoothing forecasting model
Основополагащ източник	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.	Brown, R. G. (1959). Statistical Forecasting for Inventory Control. McGraw-Hill.
Други названия	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	SES, Holt's linear trend method, exponential smoothing forecasting, Basit ve Çift Üstel Düzleştirme (SES / Holt)
Свързани	5	5	4	3
Резюме	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.	Exponential smoothing is a family of basic time-series forecasting models in which each new observation updates a smoothed estimate by a weighting parameter. Simple exponential smoothing (SES), introduced by Robert G. Brown in 1959, forecasts series with a stable level, while Holt's double exponential smoothing, introduced by Charles C. Holt in 1957, adds a trend term using the parameters alpha and beta.