Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Modelo DCC-GARCH de Panel× | Modelo DCC-GARCH (Correlación Condicional Dinámica)× | |
|---|---|---|
| Campo | Econometría | Econometría |
| Familia | Regression model | Regression model |
| Año de origen | 2002 | 2002 |
| Autor original | Robert F. Engle | Robert F. Engle |
| Tipo | Multivariate volatility model | Multivariate volatility model |
| Fuente seminal≠ | Engle, R. F. (2002). Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroscedasticity models. Journal of Business and Economic Statistics, 20(3), 339-350. 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 ↗ |
| Alias | DCC-GARCH panel, panel dynamic conditional correlation, multivariate DCC-GARCH, Panel DCC | DCC-GARCH, Dynamic Conditional Correlation GARCH, Engle DCC model, multivariate DCC |
| Relacionados | 5 | 5 |
| Resumen≠ | The Panel DCC-GARCH model extends Engle's (2002) Dynamic Conditional Correlation GARCH framework to panel data settings, jointly modelling time-varying volatility and cross-sectional correlations across multiple units (countries, firms, or assets) over time. It allows pairwise correlations to vary dynamically in response to market shocks while preserving parsimony via a two-step estimation. | 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. |
| ScholarGateConjunto de datos ↗ |
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