方法对比
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| 结构性断点EGARCH模型× | 动态条件相关 (DCC-GARCH) 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≠ | 1990–1991 | 2002 |
| 提出者≠ | Nelson (1991) for EGARCH; Lamoureux and Lastrapes (1990) for break-augmented GARCH variants | Robert F. Engle |
| 类型≠ | Volatility model with structural breaks | Multivariate 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 ↗ | Engle, R. F. (2002). Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business and Economic Statistics, 20(3), 339-350. DOI ↗ |
| 别名 | SB-EGARCH, EGARCH with regime shifts, break-adjusted EGARCH, structural change EGARCH | DCC-GARCH, Dynamic Conditional Correlation GARCH, Engle DCC model, multivariate DCC |
| 相关 | 5 | 5 |
| 摘要≠ | Structural Break EGARCH combines Nelson's Exponential GARCH framework with explicit allowance for one or more structural breaks in the volatility process. By letting the intercept and persistence parameters of the log-variance equation shift at detected break dates, the model avoids the spurious long-memory and inflated persistence that standard EGARCH suffers when the data contain regime changes. | The DCC-GARCH model, introduced by Engle (2002), extends univariate GARCH to capture time-varying correlations between multiple financial time series. It decomposes the multivariate conditional covariance matrix into individual volatility processes and a dynamic correlation matrix, allowing correlations to fluctuate over time while remaining computationally tractable even with many series. |
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