방법 비교

	베이지안 GARCH 모형	EGARCH 모형 (Exponential GARCH)
분야	계량경제학	계량경제학
계열	Regression model	Regression model
기원 연도	1989–2000	1991
창시자	Geweke (1989); further developed by Nakatsuma (2000) and Bauwens & Lubrano (1998)	Daniel B. Nelson
유형	Bayesian volatility model	Volatility / conditional variance model
원전	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.
별칭	Bayesian GARCH, BGARCH, GARCH with Bayesian inference, Bayesian volatility model	Exponential GARCH, EGARCH, Nelson EGARCH, log-GARCH
관련	4	6
요약	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.