方法对比

	TGARCH 模型（阈值 GARCH）	GARCH 模型（波动率预测）
领域	计量经济学	计量经济学
方法族	Regression model	Regression model
起源年份	1993-1994	1986
提出者	Zakoian (1994); Glosten, Jagannathan & Runkle (1993)	Tim Bollerslev
类型	Asymmetric volatility model	Conditional volatility model
开创性文献	Zakoian, J.-M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931-955.	Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307–327.
别名	Threshold GARCH, TGARCH, GJR-GARCH, asymmetric GARCH	GARCH, GARCH(1,1), conditional volatility model, GARCH Modeli (Oynaklık Tahmini)
相关	6	5
摘要	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.	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.