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We propose a novel estimation procedure for scale-by-scale lead-lagrelationships of financial assets observed at high-frequency in anon-synchronous manner. The proposed estimation procedure does not require anyinterpolation processing of original datasets and is applicable to those withhighest time resolution available. Consistency of the proposed estimators isshown under the continuous-time framework that has been developed in ourprevious work Hayashi and Koike (2018). An empirical application to a quotedataset of the NASDAQ-100 assets identifies two types of lead-lag relationshipsat different time scales.

Multi-scale analysis of lead-lag relationships in high-frequency financial markets