Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| DCC-GARCH (Correlación Dinámica Condicional)× | Modelo GARCH (Predicción de Volatilidad)× | |
|---|---|---|
| Campo≠ | Finanzas | Econometría |
| Familia | Regression model | Regression model |
| Año de origen≠ | 2002 | 1986 |
| Autor original≠ | Robert F. Engle | Tim Bollerslev |
| Tipo≠ | Multivariate volatility model | Conditional volatility 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 ↗ | Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307–327. DOI ↗ |
| Alias | dynamic conditional correlation, Engle DCC, multivariate GARCH, DCC-GARCH — Dinamik Koşullu Korelasyon | GARCH, GARCH(1,1), conditional volatility model, GARCH Modeli (Oynaklık Tahmini) |
| 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. | The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model, introduced by Tim Bollerslev in 1986, models the time-varying conditional variance of a financial time series. It captures volatility clustering and the ARCH effect, and is the standard tool for estimating risk and volatility in return series. |
| ScholarGateConjunto de datos ↗ |
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