Comparar métodos

	Modelo EGARCH Bayesiano	GARCH de Correlación Condicional Dinámica Bayesiana (Bayesian DCC-GARCH)
Campo	Econometría	Econometría
Familia	Regression model	Regression model
Año de origen	1991 (EGARCH); 2000s (Bayesian estimation)	2002 (DCC); 2000s (Bayesian extension)
Autor original	Nelson (1991) for EGARCH; Bayesian inference via MCMC developed from early 2000s	Engle (2002) for DCC; Bayesian extension via MCMC literature (2000s onwards)
Tipo	Volatility model with Bayesian inference	Multivariate volatility model
Fuente seminal	Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370.	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	Bayesian EGARCH model, Bayesian Exponential GARCH, EGARCH with Bayesian estimation, B-EGARCH	Bayesian DCC-GARCH, Bayesian Dynamic Conditional Correlation, MCMC DCC-GARCH, Bayesian multivariate volatility model
Relacionados	6	6
Resumen	The Bayesian EGARCH model combines Nelson's (1991) Exponential GARCH specification — which models the log of conditional variance and captures the leverage effect — with Bayesian posterior inference via Markov Chain Monte Carlo (MCMC). This allows full uncertainty quantification of all volatility parameters, including the asymmetry coefficient, without requiring large-sample normality of the estimates.	Bayesian DCC-GARCH estimates time-varying correlations across multiple financial or economic series by combining Engle's DCC-GARCH structure with Bayesian inference. Rather than maximising a likelihood, it places prior distributions over all parameters and uses Markov Chain Monte Carlo (MCMC) sampling to produce full posterior distributions, yielding richer uncertainty quantification than classical DCC-GARCH.