Sammenlign metoder

	Bayesiansk Dynamisk Betinget Korrelations-GARCH (Bayesiansk DCC-GARCH)	Bayesiansk GARCH-model
Fagområde	Økonometri	Økonometri
Familie	Regression model	Regression model
Oprindelsesår	2002 (DCC); 2000s (Bayesian extension)	1989–2000
Ophavsperson	Engle (2002) for DCC; Bayesian extension via MCMC literature (2000s onwards)	Geweke (1989); further developed by Nakatsuma (2000) and Bauwens & Lubrano (1998)
Type	Multivariate volatility model	Bayesian volatility model
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.	Geweke, J. (1989). Exact predictive densities for linear models with ARCH disturbances. Journal of Econometrics, 40(1), 63–86.
Aliasser	Bayesian DCC-GARCH, Bayesian Dynamic Conditional Correlation, MCMC DCC-GARCH, Bayesian multivariate volatility model	Bayesian GARCH, BGARCH, GARCH with Bayesian inference, Bayesian volatility model
Relaterede	6	4
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.	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.