英文标题：
《Impact of non-stationarity on estimating and modeling empirical copulas
  of daily stock returns》
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作者：
Marcel Wollschl\\\"ager and Rudi Sch\\\"afer
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最新提交年份：
2015
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英文摘要：
  All too often measuring statistical dependencies between financial time series is reduced to a linear correlation coefficient. However this may not capture all facets of reality. We study empirical dependencies of daily stock returns by their pairwise copulas. Here we investigate particularly to which extent the non-stationarity of financial time series affects both the estimation and the modeling of empirical copulas. We estimate empirical copulas from the non-stationary, original return time series and stationary, locally normalized ones. Thereby we are able to explore the empirical dependence structure on two different scales: a global and a local one. Additionally the asymmetry of the empirical copulas is emphasized as a fundamental characteristic. We compare our empirical findings with a single Gaussian copula, with a correlation-weighted average of Gaussian copulas, with the K-copula directly addressing the non-stationarity of dependencies as a model parameter, and with the skewed Student\'s t-copula. The K-copula covers the empirical dependence structure on the local scale most adequately, whereas the skewed Student\'s t-copula best captures the asymmetry of the empirical copula on the global scale.
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中文摘要：
通常，金融时间序列之间的统计相关性被简化为线性相关系数。然而，这可能无法涵盖现实的所有方面。我们研究了股票日收益率的经验相关性。在这里，我们特别研究金融时间序列的非平稳性在多大程度上影响了经验连接函数的估计和建模。我们从非平稳的原始收益时间序列和平稳的局部归一化时间序列估计经验连接函数。因此，我们能够在两个不同的尺度上探索经验依赖结构：全球尺度和局部尺度。此外，经验连接的不对称性被强调为一个基本特征。我们将我们的实证结果与单个高斯copula、高斯copula的相关加权平均值、直接将依赖项的非平稳性作为模型参数的K-copula以及倾斜的Student t-copula进行比较。K-copula最充分地覆盖了局部尺度上的经验依赖结构，而倾斜学生的t-copula最能捕捉到全球尺度上经验copula的不对称性。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Statistical Finance        统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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