পদ্ধতির তুলনা করুন

	বেয়েশীয়ান ডাইনামিক কন্ডিশনাল কোরিলেশন GARCH (বেয়েশীয়ান DCC-GARCH)	ডিসি-জিএআরসিএইচ মডেল (ডাইনামিক কন্ডিশনাল কোরিলেশন)
ক্ষেত্র	অর্থমিতি	অর্থমিতি
পরিবার	Regression model	Regression model
উদ্ভবের বছর	2002 (DCC); 2000s (Bayesian extension)	2002
প্রবর্তক	Engle (2002) for DCC; Bayesian extension via MCMC literature (2000s onwards)	Robert F. Engle
ধরন	Multivariate volatility model	Multivariate volatility model
মৌলিক উৎস	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.
অপর নাম	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
সম্পর্কিত	6	5
সারসংক্ষেপ	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.