Linganisha mbinu

	Modeli ya EGARCH (Exponential GARCH)	Mfumo wa ARIMA (Autoregressive Integrated Moving Average)	Modeli wa GARCH (Utabiri wa Msukosuko)
Nyanja	Ekonometriki	Ekonometriki	Ekonometriki
Familia	Regression model	Regression model	Regression model
Mwaka wa asili	1991	1970	1986
Mwanzilishi	Daniel B. Nelson	George Box and Gwilym Jenkins	Tim Bollerslev
Aina	Volatility / conditional variance model	Time series forecasting model	Conditional volatility model
Chanzo asilia	Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370.	Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day.	Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307–327.
Majina mbadala	Exponential GARCH, EGARCH, Nelson EGARCH, log-GARCH	ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q)	GARCH, GARCH(1,1), conditional volatility model, GARCH Modeli (Oynaklık Tahmini)
Zinazohusiana	6	6	5
Muhtasari	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.	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 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.