Сравнение на методи

	Байесов динамичен модел на условна корелация GARCH (Bayesian DCC-GARCH)	Байесов TGARCH (Прагова GARCH с Байесова оценка)
Област	Иконометрия	Иконометрия
Семейство	Regression model	Regression model
Година на възникване	2002 (DCC); 2000s (Bayesian extension)	1994 / 2008
Създател	Engle (2002) for DCC; Bayesian extension via MCMC literature (2000s onwards)	Zakoian (1994) for TGARCH; Bayesian estimation formalized by Ardia (2008)
Тип	Multivariate volatility model	Volatility model with asymmetric threshold and Bayesian inference
Основополагащ източник	Engle, R. F. (2002). Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business and Economic Statistics, 20(3), 339-350.	Zakoian, J.-M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931-955.
Други названия	Bayesian DCC-GARCH, Bayesian Dynamic Conditional Correlation, MCMC DCC-GARCH, Bayesian multivariate volatility model	Bayesian TGARCH, Bayesian GJR-GARCH, Threshold GARCH with Bayesian estimation, TGARCH-B
Свързани	6	6
Резюме	Bayesian DCC-GARCH estimates time-varying correlations across multiple financial or economic series by combining Engle's DCC-GARCH structure with Bayesian inference. Rather than maximising a likelihood, it places prior distributions over all parameters and uses Markov Chain Monte Carlo (MCMC) sampling to produce full posterior distributions, yielding richer uncertainty quantification than classical DCC-GARCH.	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.