Módszerek összehasonlítása

	Exponenciális GARCH (EGARCH)	Egyszerű és Dupla Exponenciális Szintkiegyenlítés (SES / Holt)	Regresszió Ordináris Legkisebb Négyzetes (OLS) módszerrel
Tudományterület	Ökonometria	Ökonometria	Ökonometria
Módszercsalád	Regression model	Regression model	Regression model
Keletkezés éve	1991	1957	2019
Megalkotó	Nelson	Robert G. Brown (SES); Charles C. Holt (linear trend)	Wooldridge (textbook treatment); classical least squares
Típus	Conditional volatility model (asymmetric GARCH variant)	Exponential smoothing forecasting model	Linear regression
Alapmű	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
Alternatív nevek	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
Kapcsolódó	4	3	5
Összefoglaló	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).