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High-Frequency Lead-Lag Effects and Cross-Asset Linkages: A Multi-Asset Lagged Adjustment Model

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  • Giuseppe Buccheri
  • Fulvio Corsi
  • Stefano Peluso

Abstract

Motivated by the empirical evidence of high-frequency lead-lag effects and cross-asset linkages, we introduce a multi-asset price formation model which generalizes standard univariate microstructure models of lagged price adjustment. Econometric inference on such model provides: (i) a unified statistical test for the presence of lead-lag correlations in the latent price process and for the existence of a multi-asset price formation mechanism; (ii) separate estimation of contemporaneous and lagged dependencies; (iii) an unbiased estimator of the integrated covariance of the efficient martingale price process that is robust to microstructure noise, asynchronous trading, and lead-lag dependencies. Through an extensive simulation study, we compare the proposed estimator to alternative approaches and show its advantages in recovering the true lead-lag structure of the latent price process. Our application to a set of NYSE stocks provides empirical evidence for the existence of a multi-asset price formation mechanism and sheds light on its market microstructure determinants. Supplementary materials for this article are available online.

Suggested Citation

  • Giuseppe Buccheri & Fulvio Corsi & Stefano Peluso, 2021. "High-Frequency Lead-Lag Effects and Cross-Asset Linkages: A Multi-Asset Lagged Adjustment Model," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(3), pages 605-621, July.
    • Handle: RePEc:taf:jnlbes:v:39:y:2021:i:3:p:605-621
    DOI: 10.1080/07350015.2019.1697699
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    Cited by:

    1. Yichi Zhang & Mihai Cucuringu & Alexander Y. Shestopaloff & Stefan Zohren, 2023. "Robust Detection of Lead-Lag Relationships in Lagged Multi-Factor Models," Papers 2305.06704, arXiv.org, revised Sep 2023.
    2. Bangsgaard, Christine & Kokholm, Thomas, 2024. "The lead–lag relation between VIX futures and SPX futures," Journal of Financial Markets, Elsevier, vol. 67(C).
    3. Chiranjit Dutta & Kara Karpman & Sumanta Basu & Nalini Ravishanker, 2023. "Review of Statistical Approaches for Modeling High-Frequency Trading Data," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(1), pages 1-48, May.
    4. Zhou, Xinquan & Bagnarosa, Guillaume & Gohin, Alexandre & Pennings, Joost M.E. & Debie, Philippe, 2023. "Microstructure and high-frequency price discovery in the soybean complex," Journal of Commodity Markets, Elsevier, vol. 30(C).

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