方法对比

	贝叶斯动态条件相关GARCH (Bayesian DCC-GARCH)	动态条件相关 (DCC-GARCH) 模型
领域	计量经济学	计量经济学
方法族	Regression model	Regression model
起源年份	2002 (DCC); 2000s (Bayesian extension)	2002
提出者	Engle (2002) for DCC; Bayesian extension via MCMC literature (2000s onwards)	Robert F. Engle
类型	Multivariate volatility model	Multivariate volatility model
开创性文献	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.	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.
别名	Bayesian DCC-GARCH, Bayesian Dynamic Conditional Correlation, MCMC DCC-GARCH, Bayesian multivariate volatility model	DCC-GARCH, Dynamic Conditional Correlation GARCH, Engle DCC model, multivariate DCC
相关	6	5
摘要	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 DCC-GARCH model, introduced by Engle (2002), extends univariate GARCH to capture time-varying correlations between multiple financial time series. It decomposes the multivariate conditional covariance matrix into individual volatility processes and a dynamic correlation matrix, allowing correlations to fluctuate over time while remaining computationally tractable even with many series.