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NAPLES;Mining the lead-lag Relationship from Non-synchronous and High-frequency Data

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Document pages: 10 pages

Abstract: In time-series analysis, the term "lead-lag effect " is used to describe adelayed effect on a given time series caused by another time series. lead-lageffects are ubiquitous in practice and are specifically critical in formulatinginvestment strategies in high-frequency trading. At present, there are threemajor challenges in analyzing the lead-lag effects. First, in practicalapplications, not all time series are observed synchronously. Second, the sizeof the relevant dataset and rate of change of the environment is increasinglyfaster, and it is becoming more difficult to complete the computation within aparticular time limit. Third, some lead-lag effects are time-varying and onlylast for a short period, and their delay lengths are often affected by externalfactors. In this paper, we propose NAPLES (Negative And Positive lead-lagEStimator), a new statistical measure that resolves all these problems. Throughexperiments on artificial and real datasets, we demonstrate that NAPLES has astrong correlation with the actual lead-lag effects, including those triggeredby significant macroeconomic announcements.

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