Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Modelo DCC-GARCH Não Linear (Correlação Condicional Dinâmica Assimétrica)× | Modelo DCC-GARCH (Correlação Condicional Dinâmica)× | |
|---|---|---|
| Área | Econometria | Econometria |
| Família | Regression model | Regression model |
| Ano de origem≠ | 2006 | 2002 |
| Autor original≠ | Cappiello, Engle & Sheppard | Robert F. Engle |
| Tipo≠ | Multivariate volatility and correlation model | Multivariate volatility model |
| Fonte seminal≠ | Cappiello, L., Engle, R. F., & Sheppard, K. (2006). Asymmetric dynamics in the correlations of global equity and bond returns. Journal of Financial Econometrics, 4(4), 537–572. 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 ↗ |
| Outros nomes | ADCC-GARCH, Asymmetric DCC-GARCH, NL-DCC-GARCH, Nonlinear Asymmetric DCC | DCC-GARCH, Dynamic Conditional Correlation GARCH, Engle DCC model, multivariate DCC |
| Relacionados≠ | 2 | 5 |
| Resumo≠ | The Nonlinear DCC-GARCH model extends Engle's (2002) Dynamic Conditional Correlation framework by allowing correlations to respond asymmetrically to negative versus positive return shocks. Proposed by Cappiello, Engle, and Sheppard (2006), it is the standard tool for measuring time-varying co-movement and contagion effects in multivariate financial time series when bad news is expected to increase correlations more than good news. | 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 dados ↗ |
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