Regression modelEconometrics / time series
DCC-GARCH Model (Dynamic Conditional Correlation)
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.
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Sources
- 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: 10.1198/073500102288618487 ↗
- Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987-1007. DOI: 10.2307/1912773 ↗
Related methods
Referenced by
ARCH modelBayesian ARCH modelBayesian DCC-GARCHBayesian TGARCHEGARCH modelFourier DCC-GARCHFourier GARCH ModelFourier TGARCHNonlinear DCC-GARCH modelNonlinear GARCH modelPanel DCC-GARCHPanel GARCH modelQuantile-on-Quantile RegressionRobust DCC-GARCHRobust EGARCHRobust TGARCHStructural break DCC-GARCHStructural Break EGARCHTGARCH modelTime-varying parameter DCC-GARCH model