高频夏普比率是衡量收益和风险的指标，可以避免估计高维分析中的协方差矩阵，所以在目前的投资组合构建方法中被普遍使用。近年提出的D-SEV方法，通过测量股票收益和高频夏普比率指数之间的相关性，来进一步构建投资组合。然而，D-SEV中用来度量股票与高频夏普比率的相关性的方法存在一些问题，如缺乏稳健性和计算速度慢。在本文中，使用由Sourav Chatterjee提出的新的相关系数来代替。新的相关系数保证了稳健性，特别是它可以降低异常值对相关性的影响，比如对资产价格有很大影响的重大事件。同时它的计算速度也非常快。大量的模拟表明，新的相关系数在几个不同的模型中的表现优于D-SEV和其他传统方法。2019年和2020年的上证和深证股市数据也显示，新的相关系数选择的资产组合比D-SEV选择的资产组合的年化收益高出8%，同时也拥有较高的夏普比率。
High frequency Sharpe ratio, a measure of return and risk, is commonly used in current portfolio construction method since it can avoid covariance matrix in high dimensional analysis. The newly proposed D-SEV measures the correlation between stock's return and high frequency Sharpe ratio index to further construct portfolio. However, there are some problems with the measure used in D-SEV, such as its lack of robustness and slow computational speed. In this paper, we propose to use a new correlation coefficient proposed by Sourav Chatterjee instead. The new correlation coefficient guarantee robustness, specifically it can reduce the impact of abnormal data on correlation, such as significant events that have a large impact on the asset prices. It is also extremely fast in its calculations. Extensive simulation demonstrate that new correlation coefficient outperforms D-SEV and other traditional methods in several different models. Actual Shanghai Securities Exchange (SSE) and Shenzhen Securities Exchange (SZSE) stock market data for 2019 and 2020 also show that the assets selected by new correlation coefficient earns 8% more excess annualized return than D-SEV, while it also owns a higher Sharpe ratio.
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