Dynamic Conditional Correlation
提出一类新的多元模型：动态条件相关模型，它结合了单变量GARCH的灵活性和简洁的相关参数模型，可通过两步法简单估计，并在多种场景下表现良好。
Time varying correlations are often estimated with multivariate generalized autoregressive conditional heteroskedasticity (GARCH) models that are linear in squares and cross products of the data. A new class of multivariate models called dynamic conditional correlation models is proposed. These have the flexibility of univariate GARCH models coupled with parsimonious parametric models for the correlations. They are not linear but can often be estimated very simply with univariate or two-step methods based on the likelihood function. It is shown that they perform well in a variety of situations and provide sensible empirical results.