Comparer des méthodes

	Modèle GARCH (Prévision de la volatilité)	Régression par Moindres Carrés Ordinaires (MCO)
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	1986	2019
Auteur d'origine	Tim Bollerslev	Wooldridge (textbook treatment); classical least squares
Type	Conditional volatility model	Linear regression
Source fondatrice	Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307–327.	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)	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Apparentées	5	5
Résumé	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.	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).