Comparer des méthodes

	Modèle EGARCH bayésien	TGARCH bayésien (Seuil GARCH avec estimation bayésienne)
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	1991 (EGARCH); 2000s (Bayesian estimation)	1994 / 2008
Auteur d'origine	Nelson (1991) for EGARCH; Bayesian inference via MCMC developed from early 2000s	Zakoian (1994) for TGARCH; Bayesian estimation formalized by Ardia (2008)
Type	Volatility model with Bayesian inference	Volatility model with asymmetric threshold and Bayesian inference
Source fondatrice	Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370.	Zakoian, J.-M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931-955.
Alias	Bayesian EGARCH model, Bayesian Exponential GARCH, EGARCH with Bayesian estimation, B-EGARCH	Bayesian TGARCH, Bayesian GJR-GARCH, Threshold GARCH with Bayesian estimation, TGARCH-B
Apparentées	6	6
Résumé	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.	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.