英文标题：
《Emergence of correlations between securities at short time scales》
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作者：
S. Valeyre, D. S. Grebenkov, and S. Aboura
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最新提交年份：
2018
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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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中文摘要：
相关矩阵是最优投资组合配置和风险管理的关键要素。特别是，对应于大特征值的相关矩阵的特征向量可用于识别市场模式、部门和风格因素。我们研究这些特征值如何依赖于美国市场证券回报的时间尺度。为此，将533只最大的美国股票的一分钟收益率在不同的时间尺度上进行聚合，并用于估计相关矩阵及其光谱特性。我们提出了一个简单的超前-滞后因子模型来捕获和再现观测到的特征值的时间尺度依赖性。我们揭示了随着时间尺度的增加，几个主要特征值的出现。这一重要发现证明，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。
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分类信息：

一级分类：Quantitative Finance 数量金融学
二级分类：Trading and Market Microstructure 交易与市场微观结构
分类描述：Market microstructure, liquidity, exchange and auction design, automated trading, agent-based modeling and market-making
市场微观结构，流动性，交易和拍卖设计，自动化交易，基于代理的建模和做市
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