This paper proposes parametric two-step procedures for assessing the stability of cross-sectional dependency measures in the presence of potential breaks in the marginal distributions. The procedures are based on formerly proposed sup-LR tests in which restricted and unrestricted likelihood functions are compared with each other. First, we show theoretically that standard asymptotics do not hold in this situation. We propose a suitable bootstrap scheme and derive test statistics in different commonly used settings. The properties of the test statistics and precision of the associated changepoint estimator are analyzed and compared with existing non-parametric methods in various Monte Carlo simulations. These studies reveal advantages in test power for higher-dimensional data and an almost uniform superiority of the sup-LR test in terms of precision of the change-point estimator. We then apply this method to equity returns of European banks during the financial crisis of 2008.

中文翻译：

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在参数模型中使用时变边际分布测试恒定横截面相关性

本文提出了参数化的两步程序，用于评估在边际分布存在潜在中断的情况下横截面依赖性度量的稳定性。这些程序基于以前提出的 sup-LR 检验，在该检验中将受限似然函数和非受限似然函数相互比较。首先，我们从理论上证明标准渐近性在这种情况下不成立。我们提出了一个合适的 bootstrap 方案，并在不同的常用设置中导出测试统计数据。分析了相关变化点估计器的测试统计量和精度的特性，并与各种蒙特卡罗模拟中的现有非参数方法进行了比较。这些研究揭示了高维数据测试能力的优势，以及 sup-LR 测试在变化点估计器精度方面几乎一致的优势。然后，我们将这种方法应用于 2008 年金融危机期间欧洲银行的股票收益。