Comparar métodos

	Modelo EGARCH Bayesiano	Modelo GARCH bayesiano
Campo	Econometría	Econometría
Familia	Regression model	Regression model
Año de origen	1991 (EGARCH); 2000s (Bayesian estimation)	1989–2000
Autor original	Nelson (1991) for EGARCH; Bayesian inference via MCMC developed from early 2000s	Geweke (1989); further developed by Nakatsuma (2000) and Bauwens & Lubrano (1998)
Tipo	Volatility model with Bayesian inference	Bayesian volatility model
Fuente seminal	Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370.	Geweke, J. (1989). Exact predictive densities for linear models with ARCH disturbances. Journal of Econometrics, 40(1), 63–86.
Alias	Bayesian EGARCH model, Bayesian Exponential GARCH, EGARCH with Bayesian estimation, B-EGARCH	Bayesian GARCH, BGARCH, GARCH with Bayesian inference, Bayesian volatility model
Relacionados	6	4
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.	The Bayesian GARCH model combines the GARCH framework for time-varying volatility with Bayesian posterior inference. Instead of maximising a likelihood, it specifies prior distributions for the GARCH parameters and draws from the resulting posterior — typically via Markov chain Monte Carlo (MCMC) — to quantify both point estimates and full uncertainty about volatility dynamics.