Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Modelo DCC-GARCH (Correlação Condicional Dinâmica)× | Autoregressores Vetoriais (VAR)× | |
|---|---|---|
| Área | Econometria | Econometria |
| Família | Regression model | Regression model |
| Ano de origem≠ | 2002 | 1980 |
| Autor original≠ | Robert F. Engle | Christopher A. Sims |
| Tipo≠ | Multivariate volatility model | Multivariate time-series model |
| Fonte 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 ↗ | Sims, C. A. (1980). Macroeconomics and Reality. Econometrica, 48(1), 1–48. DOI ↗ |
| Outros nomes | DCC-GARCH, Dynamic Conditional Correlation GARCH, Engle DCC model, multivariate DCC | VAR, VAR model, vector autoregressive model, multivariate autoregression |
| Relacionados | 5 | 5 |
| Resumo≠ | 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. | Vector Autoregression is a multivariate time-series model in which each variable is regressed on its own lags and the lags of all other variables in the system. Originally proposed by Sims (1980) as a data-driven alternative to large structural macroeconomic models, VAR has become the standard workhorse for dynamic analysis in empirical economics and finance. |
| ScholarGateConjunto de dados ↗ |
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