We propose a novel estimation procedure for scale-by-scale lead-lag relationships of financial assets observed at a high-frequency in a non-synchronous manner. The proposed estimation procedure does not require any interpolation processing of the original data and is applicable to quite fine resolution data. The validity of the proposed estimators is shown under the continuous-time framework  developed in our previous work Hayashi and Koike ( 2016 ). An empirical application shows promising results of the proposed approach .
We propose a novel estimation procedure for scale-by-scale lead-lag relationships of financial assets observed at high-frequency in a non-synchronous manner. The proposed estimation procedure does not require any interpolation processing of original datasets and is applicable to those with highest time resolution available. Consistency of the proposed estimators is shown under the continuous-time framework that has been developed in our previous work Hayashi and Koike ( 2018 ). An empirical application to a quote dataset of the NASDAQ-100 assets identifies two types of lead-lag relationships at different time scales .
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the original data
original datasets
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quite fine resolution data. The validity
those with highest time resolution available. Consistency
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that has been
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2016
2018
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shows promising results of the proposed approach
to a quote dataset of the NASDAQ-100 assets identifies two types of lead-lag relationships at different time scales