Linganisha mbinu

	Modeli wa GARCH (Utabiri wa Msukosuko)	Data ya Masafa ya Juu na Uchambuzi wa Muundo wa Soko	Urejeshaji wa Njia ya Viwango Vidogo vya Kawaida (OLS)
Nyanja	Ekonometriki	Fedha	Ekonometriki
Familia	Regression model	Regression model	Regression model
Mwaka wa asili	1986	2007	2019
Mwanzilishi	Tim Bollerslev	Hasbrouck (2007); Aït-Sahalia & Jacod (2014)	Wooldridge (textbook treatment); classical least squares
Aina	Conditional volatility model	Market microstructure / high-frequency econometrics	Linear regression
Chanzo asilia	Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307–327.	Hasbrouck, J. (2007). Empirical Market Microstructure: The Institutions, Economics, and Econometrics of Securities Trading. Oxford University Press. ISBN: 978-0195301649	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Majina mbadala	GARCH, GARCH(1,1), conditional volatility model, GARCH Modeli (Oynaklık Tahmini)	market microstructure, high-frequency financial econometrics, tick data analysis, Yüksek Frekanslı Veri ve Piyasa Mikro Yapısı	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Zinazohusiana	5	5	5
Muhtasari	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.	Market microstructure analysis studies how prices form from tick-level trade and quote data, examining order-book dynamics, the bid-ask spread, and price discovery. The modern econometric framework was set out by Hasbrouck (2007) and extended for high-frequency data by Aït-Sahalia and Jacod (2014).	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).