Porovnat metody

	Model GARCH (Predikce volatility)	Model ARIMA (autoregresní integrovaný klouzavý průměr)	Exponential GARCH (EGARCH)	Jednoduché a dvojité exponenciální vyhlazování (SES / Holt)	Regrese metodou ordinárních nejmenších čtverců (OLS)
Obor	Ekonometrie	Ekonometrie	Ekonometrie	Ekonometrie	Ekonometrie
Rodina	Regression model	Regression model	Regression model	Regression model	Regression model
Rok vzniku	1986	2015	1991	1957	2019
Tvůrce	Tim Bollerslev	Box & Jenkins (Box-Jenkins methodology)	Nelson	Robert G. Brown (SES); Charles C. Holt (linear trend)	Wooldridge (textbook treatment); classical least squares
Typ	Conditional volatility model	Univariate time-series model	Conditional volatility model (asymmetric GARCH variant)	Exponential smoothing forecasting model	Linear regression
Původní zdroj	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	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.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Další názvy	GARCH, GARCH(1,1), conditional volatility model, GARCH Modeli (Oynaklık Tahmini)	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	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)	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Příbuzné	5	5	4	3	5
Shrnutí	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.	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).	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.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).