Comparar métodos

	Modelo DCC-GARCH de Parámetros Variables en el Tiempo	Modelo GARCH (Predicción de Volatilidad)
Campo	Econometría	Econometría
Familia	Regression model	Regression model
Año de origen	2002 (DCC-GARCH); TVP extension 2010s	1986
Autor original	Robert F. Engle (DCC-GARCH); TVP extension developed in applied finance literature	Tim Bollerslev
Tipo	Multivariate volatility model with time-varying correlation	Conditional volatility model
Fuente seminal	Engle, R. (2002). Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business and Economic Statistics, 20(3), 339-350.	Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307–327.
Alias	TVP-DCC-GARCH, time-varying DCC-GARCH, dynamic conditional correlation GARCH with TVP, TVP dynamic conditional correlation model	GARCH, GARCH(1,1), conditional volatility model, GARCH Modeli (Oynaklık Tahmini)
Relacionados	4	5
Resumen	The TVP-DCC-GARCH model extends the Dynamic Conditional Correlation GARCH framework by allowing not only the pairwise correlations but also the underlying model parameters to evolve continuously over time. It captures structural shifts in volatility dynamics and cross-asset dependence, making it essential for financial risk modelling in non-stationary environments.	The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model, introduced by Tim Bollerslev in 1986, models the time-varying conditional variance of a financial time series. It captures volatility clustering and the ARCH effect, and is the standard tool for estimating risk and volatility in return series.