Bandingkan metode

	Model ARCH Bayesian	Model GARCH Bayesian
Bidang	Ekonometrika	Ekonometrika
Keluarga	Regression model	Regression model
Tahun asal	1982 (ARCH); 1989 (Bayesian estimation)	1989–2000
Pencetus	Robert F. Engle (ARCH, 1982); Bayesian treatment: John Geweke (1989)	Geweke (1989); further developed by Nakatsuma (2000) and Bauwens & Lubrano (1998)
Tipe	Volatility model with Bayesian inference	Bayesian volatility model
Sumber perintis	Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007.	Geweke, J. (1989). Exact predictive densities for linear models with ARCH disturbances. Journal of Econometrics, 40(1), 63–86.
Alias	Bayesian ARCH, ARCH with Bayesian estimation, Bayesian conditional heteroskedasticity model, B-ARCH	Bayesian GARCH, BGARCH, GARCH with Bayesian inference, Bayesian volatility model
Terkait	6	4
Ringkasan	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 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.