Confronta i metodi

	Modello TGARCH (Threshold GARCH)	Modello ARIMA (Autoregressive Integrated Moving Average)	Modello EGARCH (Exponential GARCH)
Campo	Econometria	Econometria	Econometria
Famiglia	Regression model	Regression model	Regression model
Anno di origine	1993-1994	1970	1991
Ideatore	Zakoian (1994); Glosten, Jagannathan & Runkle (1993)	George Box and Gwilym Jenkins	Daniel B. Nelson
Tipo	Asymmetric volatility model	Time series forecasting model	Volatility / conditional variance model
Fonte seminale	Zakoian, J.-M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931-955.	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.
Alias	Threshold GARCH, TGARCH, GJR-GARCH, asymmetric GARCH	ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q)	Exponential GARCH, EGARCH, Nelson EGARCH, log-GARCH
Correlati	6	6	6
Sintesi	The Threshold GARCH (TGARCH) model extends the standard GARCH framework by allowing positive and negative return shocks to have asymmetric effects on conditional variance. Negative shocks — bad news — typically amplify volatility more than positive shocks of the same magnitude, a stylised fact known as the leverage effect. TGARCH captures this asymmetry through a threshold indicator that switches on when the previous period's shock was negative.	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.