Sammenlign metoder

	Bayesiansk Dynamisk Betinget Korrelations-GARCH (Bayesiansk DCC-GARCH)	Bayesian TGARCH (Threshold GARCH med Bayesiansk Estimering)
Fagområde	Økonometri	Økonometri
Familie	Regression model	Regression model
Oprindelsesår	2002 (DCC); 2000s (Bayesian extension)	1994 / 2008
Ophavsperson	Engle (2002) for DCC; Bayesian extension via MCMC literature (2000s onwards)	Zakoian (1994) for TGARCH; Bayesian estimation formalized by Ardia (2008)
Type	Multivariate volatility model	Volatility model with asymmetric threshold and Bayesian inference
Oprindelig kilde	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.
Aliasser	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
Relaterede	6	6
Resumé	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.