Uporedite metode

	EGARCH model (eksponencijalni GARCH)	ARIMA model (Autoregresivni integrisani model pokretnih proseka)	GARCH model (predviđanje volatilnosti)
Oblast	Ekonometrija	Ekonometrija	Ekonometrija
Porodica	Regression model	Regression model	Regression model
Godina nastanka	1991	1970	1986
Tvorac	Daniel B. Nelson	George Box and Gwilym Jenkins	Tim Bollerslev
Tip	Volatility / conditional variance model	Time series forecasting model	Conditional volatility model
Temeljni izvor	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.
Drugi nazivi	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)
Srodne	6	6	5
Sažetak	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.