Módszerek összehasonlítása

	Nonlineáris TGARCH modell	EGARCH modell (Exponenciális GARCH)	TGARCH modell (küszöb GARCH)
Tudományterület	Ökonometria	Ökonometria	Ökonometria
Módszercsalád	Regression model	Regression model	Regression model
Keletkezés éve	1993–1994	1991	1993-1994
Megalkotó	Jean-Michel Zakoian; related work by Glosten, Jagannathan & Runkle	Daniel B. Nelson	Zakoian (1994); Glosten, Jagannathan & Runkle (1993)
Típus	Conditional heteroskedasticity model	Volatility / conditional variance model	Asymmetric volatility model
Alapmű	Zakoian, J.-M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931–955.	Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370.	Zakoian, J.-M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931-955.
Alternatív nevek	NL-TGARCH, Nonlinear Threshold GARCH, Asymmetric TGARCH, GJR-GARCH variant	Exponential GARCH, EGARCH, Nelson EGARCH, log-GARCH	Threshold GARCH, TGARCH, GJR-GARCH, asymmetric GARCH
Kapcsolódó	4	6	6
Összefoglaló	The Nonlinear TGARCH (Threshold GARCH) model extends the standard GARCH framework by allowing positive and negative shocks of equal magnitude to exert different effects on future volatility. It models conditional volatility in terms of the absolute value of lagged residuals split by a sign threshold, capturing the well-documented leverage effect in financial return series.	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 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.