השוואת שיטות

	מודל ARIMA (Autoregressive Integrated Moving Average)	מודל EGARCH (Exponential GARCH)
תחום	אקונומטריקה	אקונומטריקה
משפחה	Regression model	Regression model
שנת המקור	1970	1991
הוגה השיטה	George Box and Gwilym Jenkins	Daniel B. Nelson
סוג	Time series forecasting model	Volatility / conditional variance model
מקור מכונן	Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day.	Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370.
כינויים	ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q)	Exponential GARCH, EGARCH, Nelson EGARCH, log-GARCH
קשורות	6	6
תקציר	The ARIMA(p,d,q) model is the standard workhorse for univariate time series forecasting. It combines autoregressive terms (past values), differencing to induce stationarity, and moving average terms (past shocks) into a unified linear framework. Developed by Box and Jenkins (1970), it remains one of the most widely applied models in econometrics and applied statistics.	The Exponential GARCH (EGARCH) model, introduced by Nelson (1991), extends the standard GARCH framework by modelling the logarithm of conditional variance. This ensures variance is always positive without parameter constraints and, crucially, allows negative and positive shocks to have asymmetric effects on volatility — capturing the well-known leverage effect in financial markets.