In this paper we consider mixture generalized autoregressive conditional heteroskedastic models, and propose a new iteration algorithm of type EM for the estimation of model parameters. The maximum likelihood estimates are shown to be consistent, and their asymptotic properties are investigated. More precisely, we derive simple expressions in closed form for the asymptotic covariance matrix and the expected Fisher information matrix of the ML estimator. Finally, we study the model selection and propose testing procedures. A simulation study and an application to financial real-series illustrate the results.

中文翻译：

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具有财务应用的混合GARCH模型的统计推断

在本文中，我们考虑了混合广义自回归条件异方差模型，并提出了一种新的EM类型的迭代算法来估计模型参数。最大似然估计被证明是一致的，并且研​​究了它们的渐近性质。更准确地说，我们为ML估计量的渐近协方差矩阵和期望Fisher信息矩阵以封闭形式导出简单表达式。最后，我们研究模型的选择并提出测试程序。仿真研究及其在金融实数系列中的应用说明了结果。