STRONG CONSISTENCY OF ESTIMATORS FOR MULTIVARIATE ARCH MODELS
研究了多元异方差模型拟极大似然估计量的渐近性质，给出了强相合性成立的弱条件，并具体分析了常相关多元GARCH模型的参数空间。
This paper deals with the asymptotic properties of quasi-maximum likelihood estimators for multivariate heteroskedastic models. For a general model, we give conditions under which strong consistency can be obtained; unlike in the current literature, the assumptions on the existence of moments of the error term are weak, and no study of the various derivatives of the likelihood is required. Then, for a particular model, the multivariate GARCH model with constant correlation, we describe the set of parameters where these conditions hold.