Bandingkan metode

	TGARCH Bayesian (Threshold GARCH dengan Estimasi Bayesian)	Model GARCH Bayesian
Bidang	Ekonometrika	Ekonometrika
Keluarga	Regression model	Regression model
Tahun asal	1994 / 2008	1989–2000
Pencetus	Zakoian (1994) for TGARCH; Bayesian estimation formalized by Ardia (2008)	Geweke (1989); further developed by Nakatsuma (2000) and Bauwens & Lubrano (1998)
Tipe	Volatility model with asymmetric threshold and Bayesian inference	Bayesian volatility model
Sumber perintis	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
Terkait	6	4
Ringkasan	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.