Compara mètodes

	Model DCC-GARCH amb Fourier	Model GARCH de Fourier
Camp	Econometria	Econometria
Família	Regression model	Regression model
Any d'origen	2002 (DCC-GARCH); Fourier extension applied from mid-2010s onward	2000–2012
Autor original	Engle (2002) for DCC-GARCH; Fourier extension by Gallant (1981) and later applied in financial econometrics	Ludlow & Enders (2000); extended by Enders & Lee (2012) Fourier framework
Tipus	Multivariate volatility model with smooth structural breaks	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.	Ludlow, J., & Enders, W. (2000). Estimating non-linear ARMA models using Fourier coefficients. International Journal of Forecasting, 16(3), 333–347.
Àlies	Fourier DCC-GARCH, Fourier-augmented DCC-GARCH, DCC-GARCH with Fourier terms, smooth structural break DCC-GARCH	Fourier GARCH, Fourier-flexible GARCH, GARCH with Fourier terms, smooth-break GARCH
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 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.