Direct Estimation of Lead–Lag Relationships Using Multinomial Dynamic Time Warping

Katsuya Ito; Ryuta Sakemoto

doi:10.1007/s10690-019-09295-z

Direct Estimation of Lead–Lag Relationships Using Multinomial Dynamic Time Warping

Katsuya Ito, Ryuta Sakemoto

Research output: Contribution to journal › Article › peer-review

1 Citation (Scopus)

Abstract

This paper investigates the lead–lag relationships in high-frequency data. We propose multinomial dynamic time warping (MDTW) that deals with non-synchronous observation, vast data, and time-varying lead–lag. MDTW directly estimates the lead–lags without lag candidates. Its computational complexity is linear with respect to the number of observation and it does not depend on the number of lag candidates. The experiments adopting artificial data and market data illustrate the effectiveness of our method compared to the existing methods.

Original language	English
Pages (from-to)	325-342
Number of pages	18
Journal	Asia-Pacific Financial Markets
Volume	27
Issue number	3
DOIs	https://doi.org/10.1007/s10690-019-09295-z
Publication status	Published - Sep 1 2020
Externally published	Yes

Keywords

Dynamic time warping
High frequency trading
Lead–lag relationships

ASJC Scopus subject areas

Finance

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10.1007/s10690-019-09295-z

Cite this

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abstract = "This paper investigates the lead–lag relationships in high-frequency data. We propose multinomial dynamic time warping (MDTW) that deals with non-synchronous observation, vast data, and time-varying lead–lag. MDTW directly estimates the lead–lags without lag candidates. Its computational complexity is linear with respect to the number of observation and it does not depend on the number of lag candidates. The experiments adopting artificial data and market data illustrate the effectiveness of our method compared to the existing methods.",

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N2 - This paper investigates the lead–lag relationships in high-frequency data. We propose multinomial dynamic time warping (MDTW) that deals with non-synchronous observation, vast data, and time-varying lead–lag. MDTW directly estimates the lead–lags without lag candidates. Its computational complexity is linear with respect to the number of observation and it does not depend on the number of lag candidates. The experiments adopting artificial data and market data illustrate the effectiveness of our method compared to the existing methods.

AB - This paper investigates the lead–lag relationships in high-frequency data. We propose multinomial dynamic time warping (MDTW) that deals with non-synchronous observation, vast data, and time-varying lead–lag. MDTW directly estimates the lead–lags without lag candidates. Its computational complexity is linear with respect to the number of observation and it does not depend on the number of lag candidates. The experiments adopting artificial data and market data illustrate the effectiveness of our method compared to the existing methods.

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Direct Estimation of Lead–Lag Relationships Using Multinomial Dynamic Time Warping

Abstract

Keywords

ASJC Scopus subject areas

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