Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Modelo TGARCH (Threshold GARCH)× | Modelo DCC-GARCH (Correlación Condicional Dinámica)× | |
|---|---|---|
| Campo | Econometría | Econometría |
| Familia | Regression model | Regression model |
| Año de origen≠ | 1993-1994 | 2002 |
| Autor original≠ | Zakoian (1994); Glosten, Jagannathan & Runkle (1993) | Robert F. Engle |
| Tipo≠ | Asymmetric volatility model | Multivariate volatility model |
| Fuente seminal≠ | Zakoian, J.-M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931-955. 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 | Threshold GARCH, TGARCH, GJR-GARCH, asymmetric GARCH | DCC-GARCH, Dynamic Conditional Correlation GARCH, Engle DCC model, multivariate DCC |
| Relacionados≠ | 6 | 5 |
| Resumen≠ | The Threshold GARCH (TGARCH) model extends the standard GARCH framework by allowing positive and negative return shocks to have asymmetric effects on conditional variance. Negative shocks — bad news — typically amplify volatility more than positive shocks of the same magnitude, a stylised fact known as the leverage effect. TGARCH captures this asymmetry through a threshold indicator that switches on when the previous period's shock was negative. | 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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