Regression modelEconometrics / time series
贝叶斯动态条件相关GARCH (Bayesian DCC-GARCH)
贝叶斯DCC-GARCH模型通过将Engle提出的DCC-GARCH结构与贝叶斯推断相结合，来估计多个金融或经济序列之间随时间变化的相关性。该模型不最大化似然函数，而是为所有参数设定先验分布，并使用马尔可夫链蒙特卡洛 (MCMC) 抽样来生成完整的后验分布，从而提供比经典DCC-GARCH更丰富的量化不确定性。
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来源
- 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. DOI: 10.1198/073500102288618487 ↗
- Virbickaite, A., Ausin, M. C., & Galeano, P. (2015). Bayesian inference methods for univariate and multivariate GARCH models: A survey. Journal of Economic Surveys, 29(1), 76-96. DOI: 10.1111/joes.12046 ↗
如何引用本页
ScholarGate. (2026, June 3). Bayesian Dynamic Conditional Correlation GARCH Model. ScholarGate. https://scholargate.app/zh/econometrics/bayesian-dcc-garch
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
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