A component model for dynamic correlations
将波动率的成分模型思想扩展到动态相关性，提出DCC-MIDAS模型，包含短期和长期成分，通过混频数据抽样提取长期相关成分，并给出正半定条件及实证支持。
The idea of component models for volatility is extended to dynamic correlations. We propose a model of dynamic correlations with a short- and long-run component specification. We call this class of models DCC-MIDAS as the key ingredients are a combination of the Engle (2002) DCC model, the Engle and Lee (1999) component GARCH model to replace the original DCC dynamics with a component specification and the Engle, Ghysels, and Sohn (2006) GARCH-MIDAS component specification that allows us to extract a long-run correlation component via mixed data sampling. We provide a comprehensive econometric analysis of the new class of models, including conditions for positive semi-definiteness, and provide extensive empirical evidence that supports the model specification. We would like to thank the Editor, Allan Timmermann, as well as two referees for invaluable