Salīdzināt metodes

	Nelineārs GARCH modelis	TGARCH modelis (sliekšņa GARCH)
Nozare	Ekonometrija	Ekonometrija
Saime	Regression model	Regression model
Izcelsmes gads	1991-1993	1993-1994
Autors	Glosten, Jagannathan & Runkle; Nelson (1991) for EGARCH	Zakoian (1994); Glosten, Jagannathan & Runkle (1993)
Tips	Volatility model	Asymmetric volatility model
Pirmavots	Glosten, L. R., Jagannathan, R., & Runkle, D. E. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. Journal of Finance, 48(5), 1779-1801.	Zakoian, J.-M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931-955.
Citi nosaukumi	NL-GARCH, asymmetric GARCH, GJR-GARCH, nonlinear volatility model	Threshold GARCH, TGARCH, GJR-GARCH, asymmetric GARCH
Saistītās	6	6
Kopsavilkums	The Nonlinear GARCH model extends the standard GARCH framework to capture asymmetric and nonlinear responses of conditional volatility to past shocks. It allows negative returns (bad news) to amplify volatility more than positive returns of equal magnitude, a phenomenon known as the leverage effect, which is empirically pervasive 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.