  In time-series analysis, the term "lead-lag effect" is used to describe a
delayed effect on a given time series caused by another time series. lead-lag
effects are ubiquitous in practice and are specifically critical in formulating
investment strategies in high-frequency trading. At present, there are three
major challenges in analyzing the lead-lag effects. First, in practical
applications, not all time series are observed synchronously. Second, the size
of the relevant dataset and rate of change of the environment is increasingly
faster, and it is becoming more difficult to complete the computation within a
particular time limit. Third, some lead-lag effects are time-varying and only
last for a short period, and their delay lengths are often affected by external
factors. In this paper, we propose NAPLES (Negative And Positive lead-lag
EStimator), a new statistical measure that resolves all these problems. Through
experiments on artificial and real datasets, we demonstrate that NAPLES has a
strong correlation with the actual lead-lag effects, including those triggered
by significant macroeconomic announcements.
