Salīdzināt metodes

	Bayesiešu ARH modelis	Neibiešu TGARCH (Threshold GARCH ar Neibiešu novērtēšanu)
Nozare	Ekonometrija	Ekonometrija
Saime	Regression model	Regression model
Izcelsmes gads	1982 (ARCH); 1989 (Bayesian estimation)	1994 / 2008
Autors	Robert F. Engle (ARCH, 1982); Bayesian treatment: John Geweke (1989)	Zakoian (1994) for TGARCH; Bayesian estimation formalized by Ardia (2008)
Tips	Volatility model with Bayesian inference	Volatility model with asymmetric threshold and Bayesian inference
Pirmavots	Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007.	Zakoian, J.-M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931-955.
Citi nosaukumi	Bayesian ARCH, ARCH with Bayesian estimation, Bayesian conditional heteroskedasticity model, B-ARCH	Bayesian TGARCH, Bayesian GJR-GARCH, Threshold GARCH with Bayesian estimation, TGARCH-B
Saistītās	6	6
Kopsavilkums	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.	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.