Confronta i metodi

	Modello GARCH (Previsione della Volatilità)	Smorzamento Esponenziale Semplice e Doppio (SES / Holt)	Regression with Ordinary Least Squares (OLS)
Campo	Econometria	Econometria	Econometria
Famiglia	Regression model	Regression model	Regression model
Anno di origine	1986	1957	2019
Ideatore	Tim Bollerslev	Robert G. Brown (SES); Charles C. Holt (linear trend)	Wooldridge (textbook treatment); classical least squares
Tipo	Conditional volatility model	Exponential smoothing forecasting model	Linear regression
Fonte seminale	Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307–327.	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
Alias	GARCH, GARCH(1,1), conditional volatility model, GARCH Modeli (Oynaklık Tahmini)	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
Correlati	5	3	5
Sintesi	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.	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).