方法对比

	稳健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	1986–2013	1991
提出者	Boudt, Danielsson & Laurent (robust extensions); Bollerslev (standard GARCH, 1986)	Daniel B. Nelson
类型	Volatility model	Volatility / conditional variance model
开创性文献	Boudt, K., Danielsson, J., & Laurent, S. (2013). Robust forecasting of dynamic conditional correlation GARCH models. International Journal of Forecasting, 29(2), 244–257.	Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370.
别名	Robust GARCH, outlier-robust GARCH, heavy-tail GARCH, contamination-robust volatility model	Exponential GARCH, EGARCH, Nelson EGARCH, log-GARCH
相关	5	6
摘要	The Robust GARCH model extends the classical GARCH framework to handle outliers and heavy-tailed innovations that commonly appear in financial return series. By down-weighting extreme observations through a robust innovation term, it produces more reliable volatility forecasts when data contain jumps, crises, or other anomalies that would otherwise distort standard GARCH estimates.	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.