方法对比

	Fourier DCC-GARCH 模型	EGARCH model
领域	计量经济学	计量经济学
方法族	Regression model	Regression model
起源年份	2002 (DCC-GARCH); Fourier extension applied from mid-2010s onward	1991
提出者	Engle (2002) for DCC-GARCH; Fourier extension by Gallant (1981) and later applied in financial econometrics	Daniel B. Nelson
类型	Multivariate volatility model with smooth structural breaks	Volatility / conditional variance model
开创性文献	Engle, R. (2002). Dynamic conditional correlations: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business and Economic Statistics, 20(3), 339-350.	Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370.
别名	Fourier DCC-GARCH, Fourier-augmented DCC-GARCH, DCC-GARCH with Fourier terms, smooth structural break DCC-GARCH	Exponential GARCH, EGARCH, Nelson EGARCH, log-GARCH
相关	5	6
摘要	The Fourier DCC-GARCH model extends Engle's Dynamic Conditional Correlation GARCH framework by embedding Fourier trigonometric terms in the conditional mean or variance equations. This allows the model to approximate smooth, gradual structural shifts in volatility dynamics and inter-asset correlations without requiring knowledge of the number or timing of break points.	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.