Salīdzināt metodes

	Neibai's EGARCH modelis	EGARCH modelis (eksponenciālais GARCH)
Nozare	Ekonometrija	Ekonometrija
Saime	Regression model	Regression model
Izcelsmes gads	1991 (EGARCH); 2000s (Bayesian estimation)	1991
Autors	Nelson (1991) for EGARCH; Bayesian inference via MCMC developed from early 2000s	Daniel B. Nelson
Tips	Volatility model with Bayesian inference	Volatility / conditional variance model
Pirmavots	Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370.	Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370.
Citi nosaukumi	Bayesian EGARCH model, Bayesian Exponential GARCH, EGARCH with Bayesian estimation, B-EGARCH	Exponential GARCH, EGARCH, Nelson EGARCH, log-GARCH
Saistītās	6	6
Kopsavilkums	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 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.