Сравнение методов

	Байесовская динамическая условная корреляция GARCH (Bayesian DCC-GARCH)	Модель Байесовского векторного авторегрессионного анализа (BVAR)
Область	Эконометрика	Эконометрика
Семейство	Regression model	Regression model
Год появления	2002 (DCC); 2000s (Bayesian extension)	1984
Автор метода	Engle (2002) for DCC; Bayesian extension via MCMC literature (2000s onwards)	Doan, Litterman & Sims
Тип	Multivariate volatility model	Multivariate time-series model
Основополагающий источник	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.	Doan, T., Litterman, R., & Sims, C. (1984). Forecasting and conditional projection using realistic prior distributions. Econometric Reviews, 3(1), 1–100.
Другие названия	Bayesian DCC-GARCH, Bayesian Dynamic Conditional Correlation, MCMC DCC-GARCH, Bayesian multivariate volatility model	BVAR, Bayesian VAR, Bayesian vector autoregressive model, BVAR model
Связанные	6	5
Сводка	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 Vector Autoregression (BVAR) model extends the classical VAR framework by incorporating prior beliefs about the model coefficients. Priors — most commonly the Minnesota prior — shrink VAR coefficients toward economically sensible values, dramatically reducing overfitting and improving out-of-sample forecast accuracy even when the number of variables is large.