Confronta i metodi

	TGARCH Bayesiano (Threshold GARCH con Stima Bayesiana)	Modello GARCH Bayesiano
Campo	Econometria	Econometria
Famiglia	Regression model	Regression model
Anno di origine	1994 / 2008	1989–2000
Ideatore	Zakoian (1994) for TGARCH; Bayesian estimation formalized by Ardia (2008)	Geweke (1989); further developed by Nakatsuma (2000) and Bauwens & Lubrano (1998)
Tipo	Volatility model with asymmetric threshold and Bayesian inference	Bayesian volatility model
Fonte seminale	Zakoian, J.-M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931-955.	Geweke, J. (1989). Exact predictive densities for linear models with ARCH disturbances. Journal of Econometrics, 40(1), 63–86.
Alias	Bayesian TGARCH, Bayesian GJR-GARCH, Threshold GARCH with Bayesian estimation, TGARCH-B	Bayesian GARCH, BGARCH, GARCH with Bayesian inference, Bayesian volatility model
Correlati	6	4
Sintesi	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 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.