পদ্ধতির তুলনা করুন

	বেয়েশীয় GARCH মডেল	ইগার্চ মডেল (এক্সপোনেনশিয়াল GARCH)
ক্ষেত্র	অর্থমিতি	অর্থমিতি
পরিবার	Regression model	Regression model
উদ্ভবের বছর	1989–2000	1991
প্রবর্তক	Geweke (1989); further developed by Nakatsuma (2000) and Bauwens & Lubrano (1998)	Daniel B. Nelson
ধরন	Bayesian volatility model	Volatility / conditional variance model
মৌলিক উৎস	Geweke, J. (1989). Exact predictive densities for linear models with ARCH disturbances. Journal of Econometrics, 40(1), 63–86.	Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370.
অপর নাম	Bayesian GARCH, BGARCH, GARCH with Bayesian inference, Bayesian volatility model	Exponential GARCH, EGARCH, Nelson EGARCH, log-GARCH
সম্পর্কিত	4	6
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 Exponential GARCH (EGARCH) model, introduced by Nelson (1991), extends the standard GARCH framework by modelling the logarithm of conditional variance. This ensures variance is always positive without parameter constraints and, crucially, allows negative and positive shocks to have asymmetric effects on volatility — capturing the well-known leverage effect in financial markets.