Comparar métodos

	Modelo GARCH bayesiano	Modelo ARCH (Heterocedasticidad Autoregresiva Condicional)
Campo	Econometría	Econometría
Familia	Regression model	Regression model
Año de origen	1989–2000	1982
Autor original	Geweke (1989); further developed by Nakatsuma (2000) and Bauwens & Lubrano (1998)	Robert F. Engle
Tipo	Bayesian volatility model	Conditional volatility model
Fuente seminal	Geweke, J. (1989). Exact predictive densities for linear models with ARCH disturbances. Journal of Econometrics, 40(1), 63–86.	Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007.
Alias	Bayesian GARCH, BGARCH, GARCH with Bayesian inference, Bayesian volatility model	ARCH, autoregressive conditional heteroskedasticity, Engle ARCH, conditional variance model
Relacionados	4	6
Resumen	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.	The ARCH model, introduced by Robert Engle in 1982, captures time-varying volatility in financial and macroeconomic time series. It models the conditional variance of today's error as a function of past squared errors, explaining why volatile periods cluster together — a phenomenon known as volatility clustering.