방법 비교

	베이지안 DCC-GARCH (Bayesian DCC-GARCH)	베이지안 EGARCH 모형
분야	계량경제학	계량경제학
계열	Regression model	Regression model
기원 연도	2002 (DCC); 2000s (Bayesian extension)	1991 (EGARCH); 2000s (Bayesian estimation)
창시자	Engle (2002) for DCC; Bayesian extension via MCMC literature (2000s onwards)	Nelson (1991) for EGARCH; Bayesian inference via MCMC developed from early 2000s
유형	Multivariate volatility model	Volatility model with Bayesian inference
원전	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.	Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370.
별칭	Bayesian DCC-GARCH, Bayesian Dynamic Conditional Correlation, MCMC DCC-GARCH, Bayesian multivariate volatility model	Bayesian EGARCH model, Bayesian Exponential GARCH, EGARCH with Bayesian estimation, B-EGARCH
관련	6	6
요약	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 EGARCH model combines Nelson's (1991) Exponential GARCH specification — which models the log of conditional variance and captures the leverage effect — with Bayesian posterior inference via Markov Chain Monte Carlo (MCMC). This allows full uncertainty quantification of all volatility parameters, including the asymmetry coefficient, without requiring large-sample normality of the estimates.