短时间尺度下证券之间相关性的出现
Emergence of correlations between securities at short time scales
摘要：
相关矩阵是投资组合优化配置和风险管理的关键要素。特别地，对应于大特征值的相关矩阵的特征向量可以用于识别市场模式、部门和风格因素。我们研究这些特征值如何依赖于在美国市场的证券收益率的时间尺度。为此，最大的533只美国股票的一分钟收益率在不同的时间尺度上被聚合，并用于估计相关矩阵及其谱特性。我们提出了一个简单的领先滞后因子模型捕捉和再现所观察到的特征值的时间尺度依赖性。我们揭示了几个主导特征值的出现，随着时间尺度的增加。这一重要发现表明，决定证券相关性结构的基本经济和金融机制也取决于时间尺度。
英文摘要：
The correlation matrix is the key element in optimal portfolio allocation and risk management. In particular, the eigenvectors of the correlation matrix corresponding to large eigenvalues can be used to identify the market mode, sectors and style factors. We investigate how these eigenvalues depend on the time scale of securities returns in the U.S. market. For this purpose, one-minute returns of the largest 533 U.S. stocks are aggregated at different time scales and used to estimate the correlation matrix and its spectral properties. We propose a simple lead-lag factor model to capture and reproduce the observed time-scale dependence of eigenvalues. We reveal the emergence of several dominant eigenvalues as the time scale increases. This important finding evidences that the underlying economic and financial mechanisms determining the correlation structure of securities depend as well on time scales.
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