方法对比

	动态条件相关 (DCC-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	2002	1993-1994
提出者	Robert F. Engle	Zakoian (1994); Glosten, Jagannathan & Runkle (1993)
类型	Multivariate volatility model	Asymmetric volatility model
开创性文献	Engle, R. F. (2002). Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business and Economic Statistics, 20(3), 339-350.	Zakoian, J.-M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931-955.
别名	DCC-GARCH, Dynamic Conditional Correlation GARCH, Engle DCC model, multivariate DCC	Threshold GARCH, TGARCH, GJR-GARCH, asymmetric GARCH
相关	5	6
摘要	The DCC-GARCH model, introduced by Engle (2002), extends univariate GARCH to capture time-varying correlations between multiple financial time series. It decomposes the multivariate conditional covariance matrix into individual volatility processes and a dynamic correlation matrix, allowing correlations to fluctuate over time while remaining computationally tractable even with many series.	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.