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	Uogólniona warunkowa heteroskedastyczność z autoregresją (GARCH)	Model ARIMA (Autoregressive Integrated Moving Average)	DCC-GARCH (Dynamic Conditional Correlation)	Wygładzanie wykładnicze proste i podwójne (SES / Holt)
Dziedzina	Ekonometria	Ekonometria	Finanse	Ekonometria
Rodzina	Regression model	Regression model	Regression model	Regression model
Rok powstania	1986	2015	2002	1957
Twórca	Tim Bollerslev	Box & Jenkins (Box-Jenkins methodology)	Robert F. Engle	Robert G. Brown (SES); Charles C. Holt (linear trend)
Typ	Conditional volatility model	Univariate time-series model	Multivariate volatility model	Exponential smoothing forecasting model
Źródło pierwotne	Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307-327.	Box, G. E. P., Jenkins, G. M., Reinsel, G. C. & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley. ISBN: 978-1118675021	Engle, R. (2002). Dynamic Conditional Correlation: A Simple Class of Multivariate GARCH Models. Journal of Business & Economic Statistics, 20(3), 339-350.	Brown, R. G. (1959). Statistical Forecasting for Inventory Control. McGraw-Hill.
Inne nazwy	GARCH(1,1), generalized ARCH, conditional volatility model, GARCH Modeli	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	dynamic conditional correlation, Engle DCC, multivariate GARCH, DCC-GARCH — Dinamik Koşullu Korelasyon	SES, Holt's linear trend method, exponential smoothing forecasting, Basit ve Çift Üstel Düzleştirme (SES / Holt)
Pokrewne	5	5	5	3
Podsumowanie	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.	ARIMA is a univariate time-series forecasting model that combines autoregressive, integrated (differencing), and moving-average components to predict a single continuous series from its own past. It is the centrepiece of the Box-Jenkins methodology set out in Box, Jenkins, Reinsel & Ljung's Time Series Analysis (5th ed., 2015).	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.	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.