GO‐GARCH: a multivariate generalized orthogonal GARCH model
提出一种新的多元GARCH模型，允许用较多自由度参数化大协方差矩阵，同时保持估计可行性；通过先利用无条件信息减少条件信息估计参数数量，避免算法收敛困难。
Abstract Multivariate GARCH specifications are typically determined by means of practical considerations such as the ease of estimation, which often results in a serious loss of generality. A new type of multivariate GARCH model is proposed, in which potentially large covariance matrices can be parameterized with a fairly large degree of freedom while estimation of the parameters remains feasible. The model can be seen as a natural generalization of the O‐GARCH model, while it is nested in the more general BEKK model. In order to avoid convergence difficulties of estimation algorithms, we propose to exploit unconditional information first, so that the number of parameters that need to be estimated by means of conditional information is more than halved. Both artificial and empirical examples are included to illustrate the model. Copyright © 2002 John Wiley & Sons, Ltd.