方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 稳健 EGARCH 模型× | 稳健TGARCH× | |
|---|---|---|
| 领域 | 计量经济学 | 计量经济学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 2008 | 1994–2000s |
| 提出者≠ | Nelson (1991) for EGARCH; robust adaptation via Muler & Yohai (2008) and related authors | Zakoian (1994) for TGARCH; robust extensions developed through quasi-maximum likelihood and M-estimation literature |
| 类型≠ | Robust volatility model | Volatility model with asymmetry and robust estimation |
| 开创性文献≠ | Muler, N., & Yohai, V. J. (2008). Robust estimates for GARCH models. Journal of Statistical Planning and Inference, 138(10), 2918–2940. DOI ↗ | Zakoian, J.-M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931–955. DOI ↗ |
| 别名 | Robust EGARCH model, outlier-robust EGARCH, robust exponential GARCH, REGARCH | robust GJR-GARCH, robust threshold GARCH, heavy-tail TGARCH, outlier-robust TGARCH |
| 相关 | 6 | 6 |
| 摘要≠ | Robust EGARCH extends Nelson's (1991) Exponential GARCH model by replacing standard quasi-maximum likelihood estimation with outlier-resistant procedures — typically bounded-influence or M-estimation — so that a small fraction of extreme observations or data errors cannot distort the estimated volatility dynamics or the leverage effect. | Robust TGARCH extends the Threshold GARCH model by replacing the conventional maximum likelihood objective with an estimator that is resistant to heavy-tailed innovations and outlying observations. It captures asymmetric volatility responses — where negative shocks amplify variance more than positive shocks — while remaining reliable when the return distribution deviates strongly from normality. |
| ScholarGate数据集 ↗ |
|
|