方法对比

	傅里叶 GARCH 模型	TGARCH 模型（阈值 GARCH）
领域	计量经济学	计量经济学
方法族	Regression model	Regression model
起源年份	2000–2012	1993-1994
提出者	Ludlow & Enders (2000); extended by Enders & Lee (2012) Fourier framework	Zakoian (1994); Glosten, Jagannathan & Runkle (1993)
类型	Volatility model	Asymmetric volatility model
开创性文献	Ludlow, J., & Enders, W. (2000). Estimating non-linear ARMA models using Fourier coefficients. International Journal of Forecasting, 16(3), 333–347.	Zakoian, J.-M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931-955.
别名	Fourier GARCH, Fourier-flexible GARCH, GARCH with Fourier terms, smooth-break GARCH	Threshold GARCH, TGARCH, GJR-GARCH, asymmetric GARCH
相关	5	6
摘要	The Fourier GARCH model embeds trigonometric Fourier terms into a standard GARCH framework to capture smooth, gradual shifts in the conditional variance process without requiring knowledge of exact structural break dates. By approximating unknown break patterns with sinusoidal functions, it jointly models volatility clustering and time-varying unconditional variance.	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.