Full BEKK的虚构

Chia‐Lin Chang, M. McAleer
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引用次数: 2

摘要

本文的目的是证明单变量GARCH不是多元GARCH的特例,特别是Full BEKK模型,除了在随机系数自回归系数矩阵的非对角元素的参数限制下,提供了由潜在随机系数自回归过程产生的正则性条件。并且在适当的参数限制下,(拟)极大似然估计具有有效的渐近性质。本文讨论了单变量和多变量GARCH模型的随机过程、正则性条件和渐近性质。结果表明,在实际中几乎完全估计的Full BEKK模型没有潜在的随机过程、正则性条件或渐近性质。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Fiction of Full BEKK
The purpose of the paper is to show that univariate GARCH is not a special case of multivariate GARCH, specifically the Full BEKK model, except under parametric restrictions on the off-diagonal elements of the random coefficient autoregressive coefficient matrix, provides the regularity conditions that arise from the underlying random coefficient autoregressive process, and for which the (quasi-) maximum likelihood estimates have valid asymptotic properties under the appropriate parametric restrictions. The paper provides a discussion of the stochastic processes, regularity conditions, and asymptotic properties of univariate and multivariate GARCH models. It is shown that the Full BEKK model, which in practice is estimated almost exclusively, has no underlying stochastic process, regularity conditions, or asymptotic properties.
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