השוואת שיטות

	מודל ARIMA (Autoregressive Integrated Moving Average)	מודל GARCH (חיזוי תנודתיות)	ניתוח נתונים בתדר גבוה ומיקרו-מבנה שוק
תחום	אקונומטריקה	אקונומטריקה	מימון
משפחה	Regression model	Regression model	Regression model
שנת המקור	2015	1986	2007
הוגה השיטה	Box & Jenkins (Box-Jenkins methodology)	Tim Bollerslev	Hasbrouck (2007); Aït-Sahalia & Jacod (2014)
סוג	Univariate time-series model	Conditional volatility model	Market microstructure / high-frequency econometrics
מקור מכונן	Box, G. E. P., Jenkins, G. M., Reinsel, G. C. & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley. ISBN: 978-1118675021	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
כינויים	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	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
קשורות	5	5	5
תקציר	ARIMA is a univariate time-series forecasting model that combines autoregressive, integrated (differencing), and moving-average components to predict a single continuous series from its own past. It is the centrepiece of the Box-Jenkins methodology set out in Box, Jenkins, Reinsel & Ljung's Time Series Analysis (5th ed., 2015).	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).