Comparar métodos

	Modelo ARCH Bayesiano	Modelo GARCH (Previsão de Volatilidade)
Área	Econometria	Econometria
Família	Regression model	Regression model
Ano de origem	1982 (ARCH); 1989 (Bayesian estimation)	1986
Autor original	Robert F. Engle (ARCH, 1982); Bayesian treatment: John Geweke (1989)	Tim Bollerslev
Tipo	Volatility model with Bayesian inference	Conditional volatility model
Fonte seminal	Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007.	Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307–327.
Outros nomes	Bayesian ARCH, ARCH with Bayesian estimation, Bayesian conditional heteroskedasticity model, B-ARCH	GARCH, GARCH(1,1), conditional volatility model, GARCH Modeli (Oynaklık Tahmini)
Relacionados	6	5
Resumo	The Bayesian ARCH model estimates Engle's Autoregressive Conditional Heteroskedasticity specification within a Bayesian framework. Instead of maximising a likelihood, it combines a prior distribution over the volatility parameters with the data likelihood to obtain a full posterior distribution, providing richer uncertainty quantification than classical maximum-likelihood ARCH.	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.