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Testing for constant correlation of filtered series under structural change

机译：结构变化下过滤系列恒定相关性的测试

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摘要

The paper proposes a test for constant correlations that allow for breaks at unknown times in the marginal means and variances. Theoretically and in an application to US and German stock returns, we find that not accounting for changes in the marginal moments has severe consequences. This is because incorrect standardization of the series transfers to the sample correlations onto which the tests are built. Correcting for variance breaks at unknown time will have an asymptotic effect. To discuss adjustments, we tackle the issue more generally by considering partial-sums-based inference on moment properties of unobserved processes that is conducted on the basis of estimated counterparts obtained in a preliminary step. The paper gives a characterization of the conditions under which the effect of filtering does not vanish asymptotically. The analysis extends to models with breaks in parameters at estimated time.
机译：本文提出了一种恒定相关性，其允许在边缘手段和差异中的未知时间发生故障。理论上和申请给我们和德国股票回报，我们发现没有考虑边际时刻的变化具有严重的后果。这是因为串联转移到测试所在的样本相关性的错误标准化不正确。纠正在未知时间下的差异突破将具有渐近效应。为了讨论调整，我们更普遍地解决问题，通过考虑基于部分和基于初步步骤中获得的估计对应物进行的不观察过程的瞬间属性来解决问题。本文给出了滤波效果不会消失渐近的条件的表征。该分析延伸到估计时间的参数中断裂的模型。

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