Salīdzināt metodes

	Neibiešu TGARCH (Threshold GARCH ar Neibiešu novērtēšanu)	Bayesiešu ARH modelis
Nozare	Ekonometrija	Ekonometrija
Saime	Regression model	Regression model
Izcelsmes gads	1994 / 2008	1982 (ARCH); 1989 (Bayesian estimation)
Autors	Zakoian (1994) for TGARCH; Bayesian estimation formalized by Ardia (2008)	Robert F. Engle (ARCH, 1982); Bayesian treatment: John Geweke (1989)
Tips	Volatility model with asymmetric threshold and Bayesian inference	Volatility model with Bayesian inference
Pirmavots	Zakoian, J.-M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931-955.	Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007.
Citi nosaukumi	Bayesian TGARCH, Bayesian GJR-GARCH, Threshold GARCH with Bayesian estimation, TGARCH-B	Bayesian ARCH, ARCH with Bayesian estimation, Bayesian conditional heteroskedasticity model, B-ARCH
Saistītās	6	6
Kopsavilkums	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 ARCH model estimates Engle's Autoregressive Conditional Heteroskedasticity specification within a Bayesian framework. Instead of maximising a likelihood, it combines a prior distribution over the volatility parameters with the data likelihood to obtain a full posterior distribution, providing richer uncertainty quantification than classical maximum-likelihood ARCH.