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动态条件相关 (DCC-GARCH) 模型

由 Engle (2002) 提出的 DCC-GARCH 模型将单变量 GARCH 模型扩展到能够捕捉多个金融时间序列之间随时间变化的协方差。它将多元条件协方差矩阵分解为个体波动率过程和一个动态相关性矩阵,从而允许协方差随时间波动,同时即使在序列很多的情况下也保持计算上的可行性。

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来源

  1. 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
  2. Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987-1007. DOI: 10.2307/1912773

如何引用本页

ScholarGate. (2026, June 3). Dynamic Conditional Correlation Generalized Autoregressive Conditional Heteroscedasticity Model. ScholarGate. https://scholargate.app/zh/econometrics/dcc-garch-model

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被引用于

ScholarGateDCC-GARCH model (Dynamic Conditional Correlation Generalized Autoregressive Conditional Heteroscedasticity Model). 于 2026-06-15 检索自 https://scholargate.app/zh/econometrics/dcc-garch-model · 数据集: https://doi.org/10.5281/zenodo.20539026