Sammenlign metoder

	ARCH-modell (Autoregressive Conditional Heteroskedasticity)	TGARCH-modell (Threshold GARCH)
Fagfelt	Økonometri	Økonometri
Familie	Regression model	Regression model
Opprinnelsesår	1982	1993-1994
Opphavsperson	Robert F. Engle	Zakoian (1994); Glosten, Jagannathan & Runkle (1993)
Type	Conditional volatility model	Asymmetric volatility model
Opprinnelig kilde	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.
Alias	ARCH, autoregressive conditional heteroskedasticity, Engle ARCH, conditional variance model	Threshold GARCH, TGARCH, GJR-GARCH, asymmetric GARCH
Relaterte	6	6
Sammendrag	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.