পদ্ধতির তুলনা করুন

	বেইসিয়ান ইজিএআরসিএইচ মডেল	বেয়েশীয় GARCH মডেল
ক্ষেত্র	অর্থমিতি	অর্থমিতি
পরিবার	Regression model	Regression model
উদ্ভবের বছর	1991 (EGARCH); 2000s (Bayesian estimation)	1989–2000
প্রবর্তক	Nelson (1991) for EGARCH; Bayesian inference via MCMC developed from early 2000s	Geweke (1989); further developed by Nakatsuma (2000) and Bauwens & Lubrano (1998)
ধরন	Volatility model with Bayesian inference	Bayesian volatility model
মৌলিক উৎস	Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370.	Geweke, J. (1989). Exact predictive densities for linear models with ARCH disturbances. Journal of Econometrics, 40(1), 63–86.
অপর নাম	Bayesian EGARCH model, Bayesian Exponential GARCH, EGARCH with Bayesian estimation, B-EGARCH	Bayesian GARCH, BGARCH, GARCH with Bayesian inference, Bayesian volatility model
সম্পর্কিত	6	4
সারসংক্ষেপ	The Bayesian EGARCH model combines Nelson's (1991) Exponential GARCH specification — which models the log of conditional variance and captures the leverage effect — with Bayesian posterior inference via Markov Chain Monte Carlo (MCMC). This allows full uncertainty quantification of all volatility parameters, including the asymmetry coefficient, without requiring large-sample normality of the estimates.	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.