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| 时变参数DCC-GARCH模型× | 动态因子模型× | |
|---|---|---|
| 领域 | 计量经济学 | 计量经济学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 2002 (DCC-GARCH); TVP extension 2010s | 2002 |
| 提出者≠ | Robert F. Engle (DCC-GARCH); TVP extension developed in applied finance literature | James Stock & Mark Watson |
| 类型≠ | Multivariate volatility model with time-varying correlation | Latent-factor time-series model |
| 开创性文献≠ | Engle, R. (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 ↗ | Stock, J. H., & Watson, M. W. (2002). Macroeconomic forecasting using diffusion indexes. Journal of Business & Economic Statistics, 20(2), 147–162. DOI ↗ |
| 别名 | TVP-DCC-GARCH, time-varying DCC-GARCH, dynamic conditional correlation GARCH with TVP, TVP dynamic conditional correlation model | Diffusion Index Model, Large-Scale Factor Model, Approximate Factor Model, Dinamik Faktör Modeli |
| 相关≠ | 4 | 2 |
| 摘要≠ | The TVP-DCC-GARCH model extends the Dynamic Conditional Correlation GARCH framework by allowing not only the pairwise correlations but also the underlying model parameters to evolve continuously over time. It captures structural shifts in volatility dynamics and cross-asset dependence, making it essential for financial risk modelling in non-stationary environments. | A Dynamic Factor Model (DFM) extracts a small number of latent common factors from a large panel of economic time series and uses those factors to forecast or nowcast a target variable. Formalized for macroeconomic forecasting by James Stock and Mark Watson in their 2002 Journal of Business & Economic Statistics paper, DFMs handle hundreds of indicators simultaneously while avoiding the curse of dimensionality that plagues traditional multivariate models. |
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