Bandingkan metode

	Model GARCH Bayesian	Model GARCH (Peramalan Volatilitas)
Bidang	Ekonometrika	Ekonometrika
Keluarga	Regression model	Regression model
Tahun asal	1989–2000	1986
Pencetus	Geweke (1989); further developed by Nakatsuma (2000) and Bauwens & Lubrano (1998)	Tim Bollerslev
Tipe	Bayesian volatility model	Conditional volatility model
Sumber perintis	Geweke, J. (1989). Exact predictive densities for linear models with ARCH disturbances. Journal of Econometrics, 40(1), 63–86.	Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307–327.
Alias	Bayesian GARCH, BGARCH, GARCH with Bayesian inference, Bayesian volatility model	GARCH, GARCH(1,1), conditional volatility model, GARCH Modeli (Oynaklık Tahmini)
Terkait	4	5
Ringkasan	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 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.