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。
| 非线性EGARCH模型× | GARCH 模型（波动率预测）× | |
|---|---|---|
| 领域 | 计量经济学 | 计量经济学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 1991 | 1986 |
| 提出者≠ | Daniel B. Nelson | Tim Bollerslev |
| 类型 | Conditional volatility model | Conditional volatility model |
| 开创性文献≠ | Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370. DOI ↗ | Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307–327. DOI ↗ |
| 别名 | NL-EGARCH, nonlinear exponential GARCH, asymmetric EGARCH, NEGARCH | GARCH, GARCH(1,1), conditional volatility model, GARCH Modeli (Oynaklık Tahmini) |
| 相关 | 5 | 5 |
| 摘要≠ | The Nonlinear EGARCH model extends Nelson's (1991) Exponential GARCH by allowing the news impact function to take a flexible nonlinear form, capturing asymmetric and nonlinear responses of conditional volatility to past shocks. It is widely used in financial econometrics to model leverage effects and complex volatility dynamics in asset returns. | The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model, introduced by Tim Bollerslev in 1986, models the time-varying conditional variance of a financial time series. It captures volatility clustering and the ARCH effect, and is the standard tool for estimating risk and volatility in return series. |
| ScholarGate数据集 ↗ |
|
|