Krahasoni metodat

	Modeli GARCH (Parashikimi i Volatilitetit)	Modeli HAR-RV i Volatilitetit të Realizuar	Regresioni me Mënyrën më të Vogël të Katrorëve (OLS)
Fusha	Ekonometri	Financë	Ekonometri
Familja	Regression model	Regression model	Regression model
Viti i origjinës	1986	2009	2019
Krijuesi	Tim Bollerslev	Fulvio Corsi	Wooldridge (textbook treatment); classical least squares
Lloji	Conditional volatility model	Linear time-series regression for volatility	Linear regression
Burimi themelues	Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307–327.	Corsi, F. (2009). A Simple Approximate Long-Memory Model of Realized Volatility. Journal of Financial Econometrics, 7(2), 174–196.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Emërtime të tjera	GARCH, GARCH(1,1), conditional volatility model, GARCH Modeli (Oynaklık Tahmini)	HAR-RV, heterogeneous autoregressive realized volatility, Corsi HAR model, HAR-RV Modeli (Heterogeneous Autoregressive Realized Volatility)	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Të lidhura	5	5	5
Përmbledhja	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.	The HAR-RV model, introduced by Fulvio Corsi in 2009, forecasts realized volatility by decomposing it into daily, weekly, and monthly components. It is a simple linear regression that mirrors how market participants with different investment horizons react to volatility, and it naturally captures the long-memory behaviour of volatility.	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).