Comparar métodos

	TGARCH bayesiano (Threshold GARCH con Estimación Bayesiana)	Modelo TGARCH (Threshold GARCH)
Campo	Econometría	Econometría
Familia	Regression model	Regression model
Año de origen	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
Fuente 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.
Alias	Bayesian TGARCH, Bayesian GJR-GARCH, Threshold GARCH with Bayesian estimation, TGARCH-B	Threshold GARCH, TGARCH, GJR-GARCH, asymmetric GARCH
Relacionados	6	6
Resumen	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.