方法对比

	非线性TGARCH模型	自回归条件异方差 (ARCH) 模型	EGARCH model
领域	计量经济学	计量经济学	计量经济学
方法族	Regression model	Regression model	Regression model
起源年份	1993–1994	1982	1991
提出者	Jean-Michel Zakoian; related work by Glosten, Jagannathan & Runkle	Robert F. Engle	Daniel B. Nelson
类型	Conditional heteroskedasticity model	Conditional volatility model	Volatility / conditional variance model
开创性文献	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.	Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370.
别名	NL-TGARCH, Nonlinear Threshold GARCH, Asymmetric TGARCH, GJR-GARCH variant	ARCH, autoregressive conditional heteroskedasticity, Engle ARCH, conditional variance model	Exponential GARCH, EGARCH, Nelson EGARCH, log-GARCH
相关	4	6	6
摘要	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 Exponential GARCH (EGARCH) model, introduced by Nelson (1991), extends the standard GARCH framework by modelling the logarithm of conditional variance. This ensures variance is always positive without parameter constraints and, crucially, allows negative and positive shocks to have asymmetric effects on volatility — capturing the well-known leverage effect in financial markets.