পদ্ধতির তুলনা করুন

	বেয়েশীয় GARCH মডেল	GARCH মডেল (ভলাটিলিটি পূর্বাভাস)
ক্ষেত্র	অর্থমিতি	অর্থমিতি
পরিবার	Regression model	Regression model
উদ্ভবের বছর	1989–2000	1986
প্রবর্তক	Geweke (1989); further developed by Nakatsuma (2000) and Bauwens & Lubrano (1998)	Tim Bollerslev
ধরন	Bayesian volatility model	Conditional volatility model
মৌলিক উৎস	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.
অপর নাম	Bayesian GARCH, BGARCH, GARCH with Bayesian inference, Bayesian volatility model	GARCH, GARCH(1,1), conditional volatility model, GARCH Modeli (Oynaklık Tahmini)
সম্পর্কিত	4	5
সারসংক্ষেপ	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.