Compara mètodes

	Model DCC-GARCH amb Fourier	Model DCC-GARCH (Dynamic Conditional Correlation)
Camp	Econometria	Econometria
Família	Regression model	Regression model
Any d'origen	2002 (DCC-GARCH); Fourier extension applied from mid-2010s onward	2002
Autor original	Engle (2002) for DCC-GARCH; Fourier extension by Gallant (1981) and later applied in financial econometrics	Robert F. Engle
Tipus	Multivariate volatility model with smooth structural breaks	Multivariate volatility model
Font seminal	Engle, R. (2002). Dynamic conditional correlations: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business and Economic Statistics, 20(3), 339-350.	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.
Àlies	Fourier DCC-GARCH, Fourier-augmented DCC-GARCH, DCC-GARCH with Fourier terms, smooth structural break DCC-GARCH	DCC-GARCH, Dynamic Conditional Correlation GARCH, Engle DCC model, multivariate DCC
Relacionats	5	5
Resum	The Fourier DCC-GARCH model extends Engle's Dynamic Conditional Correlation GARCH framework by embedding Fourier trigonometric terms in the conditional mean or variance equations. This allows the model to approximate smooth, gradual structural shifts in volatility dynamics and inter-asset correlations without requiring knowledge of the number or timing of break points.	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.