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Complex Correlation Approach for High Frequency Financial Data

M. Wilinski, Y. Ikeda, H. Aoyama

posted on 27 June 2017

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We propose a novel approach that allows to calculate Hilbert transform based complex correlation for unevenly spaced nancial data. This method is especially suitable for high frequency data, which are of a particular interest in nance. Its most important feature is the ability to take into account lead-lag relations on di erent levels, without knowing them in advance. We also present results obtained with this approach while working on Tokyo Stock Exchange intraday quotations. We show that individual sectors and subsectors tend to form important market components which may follow each other with small but signi cant delays. These components may be recognized by analysing eigenvectors of complex correlation matrix for Nikkei 225 stocks.

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