Comparar métodos

	TGARCH Bayesiano (TGARCH Limiar com Estimação Bayesiana)	Modelo TGARCH (GARCH Limiar)
Área	Econometria	Econometria
Família	Regression model	Regression model
Ano de origem	1994 / 2008	1993-1994
Autor original	Zakoian (1994) for TGARCH; Bayesian estimation formalized by Ardia (2008)	Zakoian (1994); Glosten, Jagannathan & Runkle (1993)
Tipo	Volatility model with asymmetric threshold and Bayesian inference	Asymmetric volatility model
Fonte seminal	Zakoian, J.-M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931-955.	Zakoian, J.-M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931-955.
Outros nomes	Bayesian TGARCH, Bayesian GJR-GARCH, Threshold GARCH with Bayesian estimation, TGARCH-B	Threshold GARCH, TGARCH, GJR-GARCH, asymmetric GARCH
Relacionados	6	6
Resumo	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 Threshold GARCH (TGARCH) model extends the standard GARCH framework by allowing positive and negative return shocks to have asymmetric effects on conditional variance. Negative shocks — bad news — typically amplify volatility more than positive shocks of the same magnitude, a stylised fact known as the leverage effect. TGARCH captures this asymmetry through a threshold indicator that switches on when the previous period's shock was negative.