Sammenlign metoder

	Ikke-lineær TGARCH-modell	ARCH-modell (Autoregressive Conditional Heteroskedasticity)	GARCH-modell (volatilitetsprognoser)
Fagfelt	Økonometri	Økonometri	Økonometri
Familie	Regression model	Regression model	Regression model
Opprinnelsesår	1993–1994	1982	1986
Opphavsperson	Jean-Michel Zakoian; related work by Glosten, Jagannathan & Runkle	Robert F. Engle	Tim Bollerslev
Type	Conditional heteroskedasticity model	Conditional volatility model	Conditional volatility model
Opprinnelig 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.	Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307–327.
Alias	NL-TGARCH, Nonlinear Threshold GARCH, Asymmetric TGARCH, GJR-GARCH variant	ARCH, autoregressive conditional heteroskedasticity, Engle ARCH, conditional variance model	GARCH, GARCH(1,1), conditional volatility model, GARCH Modeli (Oynaklık Tahmini)
Relaterte	4	6	5
Sammendrag	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 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.