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Dynamic Time Warping for Lead-Lag Relationships in Lagged Multi-Factor Models

Author

Listed:
  • Yichi Zhang
  • Mihai Cucuringu
  • Alexander Y. Shestopaloff
  • Stefan Zohren

Abstract

In multivariate time series systems, lead-lag relationships reveal dependencies between time series when they are shifted in time relative to each other. Uncovering such relationships is valuable in downstream tasks, such as control, forecasting, and clustering. By understanding the temporal dependencies between different time series, one can better comprehend the complex interactions and patterns within the system. We develop a cluster-driven methodology based on dynamic time warping for robust detection of lead-lag relationships in lagged multi-factor models. We establish connections to the multireference alignment problem for both the homogeneous and heterogeneous settings. Since multivariate time series are ubiquitous in a wide range of domains, we demonstrate that our algorithm is able to robustly detect lead-lag relationships in financial markets, which can be subsequently leveraged in trading strategies with significant economic benefits.

Suggested Citation

  • Yichi Zhang & Mihai Cucuringu & Alexander Y. Shestopaloff & Stefan Zohren, 2023. "Dynamic Time Warping for Lead-Lag Relationships in Lagged Multi-Factor Models," Papers 2309.08800, arXiv.org.
    • Handle: RePEc:arx:papers:2309.08800
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    File URL: http://arxiv.org/pdf/2309.08800
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    References listed on IDEAS

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    1. Jay Cao & Jacky Chen & John Hull, 2020. "A neural network approach to understanding implied volatility movements," Quantitative Finance, Taylor & Francis Journals, vol. 20(9), pages 1405-1413, September.
    2. Stefanos Bennett & Mihai Cucuringu & Gesine Reinert, 2022. "Lead-lag detection and network clustering for multivariate time series with an application to the US equity market," Papers 2201.08283, arXiv.org.
    3. Kartikay Gupta & Niladri Chatterjee, 2020. "Examining Lead-Lag Relationships In-Depth, With Focus On FX Market As Covid-19 Crises Unfolds," Papers 2004.10560, arXiv.org, revised May 2020.
    4. Tom Liu & Stefan Zohren, 2023. "Multi-Factor Inception: What to Do with All of These Features?," Papers 2307.13832, arXiv.org.
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