Salīdzināt metodes

	Neibiešu TGARCH (Threshold GARCH ar Neibiešu novērtēšanu)	Neibai's EGARCH modelis
Nozare	Ekonometrija	Ekonometrija
Saime	Regression model	Regression model
Izcelsmes gads	1994 / 2008	1991 (EGARCH); 2000s (Bayesian estimation)
Autors	Zakoian (1994) for TGARCH; Bayesian estimation formalized by Ardia (2008)	Nelson (1991) for EGARCH; Bayesian inference via MCMC developed from early 2000s
Tips	Volatility model with asymmetric threshold and Bayesian inference	Volatility model with Bayesian inference
Pirmavots	Zakoian, J.-M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931-955.	Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370.
Citi nosaukumi	Bayesian TGARCH, Bayesian GJR-GARCH, Threshold GARCH with Bayesian estimation, TGARCH-B	Bayesian EGARCH model, Bayesian Exponential GARCH, EGARCH with Bayesian estimation, B-EGARCH
Saistītās	6	6
Kopsavilkums	Bayesian TGARCH combines the Threshold GARCH volatility model — which captures the asymmetric response of volatility to positive versus negative shocks — with full Bayesian inference via Markov Chain Monte Carlo sampling. The result is a principled, uncertainty-aware framework for modeling leverage effects and fat-tailed financial returns.	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.