Bandingkan metode

	Model GARCH Bayesian	Model EGARCH (Exponential GARCH)
Bidang	Ekonometrika	Ekonometrika
Keluarga	Regression model	Regression model
Tahun asal	1989–2000	1991
Pencetus	Geweke (1989); further developed by Nakatsuma (2000) and Bauwens & Lubrano (1998)	Daniel B. Nelson
Tipe	Bayesian volatility model	Volatility / conditional variance model
Sumber perintis	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.
Alias	Bayesian GARCH, BGARCH, GARCH with Bayesian inference, Bayesian volatility model	Exponential GARCH, EGARCH, Nelson EGARCH, log-GARCH
Terkait	4	6
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 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.