方法对比
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| 稳健GARCH模型× | 自回归条件异方差 (ARCH) 模型× | |
|---|---|---|
| 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 | 1982 |
| 提出者≠ | Boudt, Danielsson & Laurent (robust extensions); Bollerslev (standard GARCH, 1986) | Robert F. Engle |
| 类型≠ | Volatility model | Conditional volatility 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. DOI ↗ | Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007. DOI ↗ |
| 别名 | Robust GARCH, outlier-robust GARCH, heavy-tail GARCH, contamination-robust volatility model | ARCH, autoregressive conditional heteroskedasticity, Engle ARCH, conditional variance model |
| 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 ARCH model, introduced by Robert Engle in 1982, captures time-varying volatility in financial and macroeconomic time series. It models the conditional variance of today's error as a function of past squared errors, explaining why volatile periods cluster together — a phenomenon known as volatility clustering. |
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