Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Modelo DCC-GARCH (Correlación Condicional Dinámica)× | Prueba de Causalidad de Granger× | |
|---|---|---|
| Campo | Econometría | Econometría |
| Familia | Regression model | Regression model |
| Año de origen≠ | 2002 | 1969 |
| Autor original≠ | Robert F. Engle | Clive W. J. Granger |
| Tipo≠ | Multivariate volatility model | Causality test (F-test on VAR) |
| Fuente seminal≠ | 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 ↗ | Granger, C. W. J. (1969). Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica, 37(3), 424–438. DOI ↗ |
| Alias | DCC-GARCH, Dynamic Conditional Correlation GARCH, Engle DCC model, multivariate DCC | Granger test, GC test, predictive causality test, Granger non-causality test |
| Relacionados | 5 | 5 |
| Resumen≠ | 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. | The Granger causality test is a statistical hypothesis test that determines whether past values of one time series help predict future values of another, beyond what that series' own past already explains. Introduced by Clive Granger in 1969, it is the standard approach for assessing predictive causality in VAR-based time-series analysis. |
| ScholarGateConjunto de datos ↗ |
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