Comparer des méthodes

	Modèle GARCH de Fourier	Modèle DCC-GARCH (Corrélation Conditionnelle Dynamique)
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	2000–2012	2002
Auteur d'origine	Ludlow & Enders (2000); extended by Enders & Lee (2012) Fourier framework	Robert F. Engle
Type	Volatility model	Multivariate volatility model
Source fondatrice	Ludlow, J., & Enders, W. (2000). Estimating non-linear ARMA models using Fourier coefficients. International Journal of Forecasting, 16(3), 333–347.	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.
Alias	Fourier GARCH, Fourier-flexible GARCH, GARCH with Fourier terms, smooth-break GARCH	DCC-GARCH, Dynamic Conditional Correlation GARCH, Engle DCC model, multivariate DCC
Apparentées	5	5
Résumé	The Fourier GARCH model embeds trigonometric Fourier terms into a standard GARCH framework to capture smooth, gradual shifts in the conditional variance process without requiring knowledge of exact structural break dates. By approximating unknown break patterns with sinusoidal functions, it jointly models volatility clustering and time-varying unconditional variance.	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.