Flexible Multivariate GARCH Modeling with an Application to International Stock Markets
提出一种新的方法，用于估计多变量GARCH(1,1)模型中的时变协方差矩阵，该方法适用于大规模问题，能保证条件协方差矩阵半正定，且不施加不切实际的先验限制，并在国际股票市场中与现有估计量进行了比较。
This paper offers a new approach to estimating time-varying covariance matrices in the framework of the diagonal-vech version of the multivariate GARCH(1, 1) model. Our method is numerically feasible for large-scale problems, produces positive semidefinite conditional covariance matrices, and does not impose unrealistic a priori restrictions. We provide an empirical application in the context of international stock markets, comparing the new estimator with a number of existing ones. © 2003 President and Fellows of Harvard College and the Massachusetts Institute of Technology.