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TESTING FOR COMMON CONDITIONALLY HETEROSKEDASTIC FACTORS

机译:测试常见的条件异方差因子

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This paper proposes a test for common conditionally heteroskedastic (CH) features in asset returns. Following Engle and Kozicki (1993), the common CH features property is expressed in terms of testable overidentifying moment restrictions. However, as we show, these moment conditions have a degenerate Jacobian matrix at the true parameter value and therefore the standard asymptotic results of Hansen (1982) do not apply. We show in this context that Hansen's (1982) J-test statistic is asymptotically distributed as the minimum of the limit of a certain random process with a markedly nonstandard distribution. If two assets are considered, this asymptotic distribution is a fifty-fifty mixture of (2)(H-1) and (2)(H), where H is the number of moment conditions, as opposed to a (2)(H-1). With more than two assets, this distribution lies between the (2)(H-p) and (2)(H) (p denotes the number of parameters). These results show that ignoring the lack of first-order identification of the moment condition model leads to oversized tests with a possibly increasing overrejection rate with the number of assets. A Monte Carlo study illustrates these findings.
机译:本文提出了对资产收益率中常见的条件异方差(CH)特征的检验。继Engle和Kozicki(1993)之后,通用的CH特征属性用可测试的过度识别力矩限制表示。但是,正如我们所示,这些矩条件在真实参数值处具有退化的雅可比矩阵,因此,Hansen(1982)的标准渐近结果不适用。我们在这种情况下表明,Hansen(1982)的J检验统计量是渐近分布的,它是某个随机过程的极限的最小值,且具有明显的非标准分布。如果考虑两个资产,则此渐近分布是(2)(H-1)和(2)(H)的五十个混合物,其中H是力矩条件的数量,而不是(2)(H -1)。如果有两个以上的资产,则此分布位于(2)(H-p)和(2)(H)之间(p表示参数数量)。这些结果表明,忽略矩条件模型的一阶识别不足会导致测试规模过大,而随着资产数量的增加,过高拒绝率可能会增加。蒙特卡洛研究表明了这些发现。

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