Sammenlign metoder

	Nonlineær TGARCH-model	ARCH-model (Autoregressiv Betinget Heteroskedasticitet)	TGARCH-model (Threshold GARCH)
Fagområde	Økonometri	Økonometri	Økonometri
Familie	Regression model	Regression model	Regression model
Oprindelsesår	1993–1994	1982	1993-1994
Ophavsperson	Jean-Michel Zakoian; related work by Glosten, Jagannathan & Runkle	Robert F. Engle	Zakoian (1994); Glosten, Jagannathan & Runkle (1993)
Type	Conditional heteroskedasticity model	Conditional volatility model	Asymmetric volatility model
Oprindelig kilde	Zakoian, J.-M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931–955.	Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007.	Zakoian, J.-M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931-955.
Aliasser	NL-TGARCH, Nonlinear Threshold GARCH, Asymmetric TGARCH, GJR-GARCH variant	ARCH, autoregressive conditional heteroskedasticity, Engle ARCH, conditional variance model	Threshold GARCH, TGARCH, GJR-GARCH, asymmetric GARCH
Relaterede	4	6	6
Resumé	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 ARCH model, introduced by Robert Engle in 1982, captures time-varying volatility in financial and macroeconomic time series. It models the conditional variance of today's error as a function of past squared errors, explaining why volatile periods cluster together — a phenomenon known as volatility clustering.	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.