Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| DCC-GARCH (Correlación Dinámica Condicional)× | Modelos de cópula (Gaussiana, t, Clayton, Gumbel, Frank)× | |
|---|---|---|
| Campo | Finanzas | Finanzas |
| Familia | Regression model | Regression model |
| Año de origen≠ | 2002 | 1959 |
| Autor original≠ | Robert F. Engle | Sklar (1959); dependence-concept treatment by Joe (1997) |
| Tipo≠ | Multivariate volatility model | Dependence model |
| Fuente seminal≠ | Engle, R. (2002). Dynamic Conditional Correlation: A Simple Class of Multivariate GARCH Models. Journal of Business & Economic Statistics, 20(3), 339-350. DOI ↗ | Sklar, A. (1959). Fonctions de répartition à n dimensions et leurs marges. Publications de l'Institut Statistique de l'Université de Paris, 8, 229-231. link ↗ |
| Alias | dynamic conditional correlation, Engle DCC, multivariate GARCH, DCC-GARCH — Dinamik Koşullu Korelasyon | copulas, dependence copulas, vine copulas, Kopula Modelleri (Gaussian, t, Clayton, Gumbel, Frank) |
| Relacionados | 5 | 5 |
| Resumen≠ | DCC-GARCH is Engle's (2002) multivariate volatility model that lets the correlations between several assets change over time. A separate univariate GARCH model is fitted to each series, and then the dynamic correlation matrix is estimated in a second, separate step. | Copula models are a family of functions that describe the dependence structure between variables separately from their individual (marginal) distributions. The foundation is Sklar's theorem (1959), which shows that any multivariate distribution can be split into its marginals plus a copula; Joe (1997) developed the modern catalogue of dependence concepts. They are central to portfolio risk and credit modelling. |
| ScholarGateConjunto de datos ↗ |
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