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A Score-Driven Conditional Correlation Model for Noisy and Asynchronous Data: An Application to High-Frequency Covariance Dynamics

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  • Giuseppe Buccheri
  • Giacomo Bormetti
  • Fulvio Corsi
  • Fabrizio Lillo

Abstract

The analysis of the intraday dynamics of covariances among high-frequency returns is challenging due to asynchronous trading and market microstructure noise. Both effects lead to significant data reduction and may severely affect the estimation of the covariances if traditional methods for low-frequency data are employed. We propose to model intraday log-prices through a multivariate local-level model with score-driven covariance matrices and to treat asynchronicity as a missing value problem. The main advantages of this approach are: (i) all available data are used when filtering the covariances, (ii) market microstructure noise is taken into account, (iii) estimation is performed by standard maximum likelihood. Our empirical analysis, performed on 1-sec NYSE data, shows that opening hours are dominated by idiosyncratic risk and that a market factor progressively emerges in the second part of the day. The method can be used as a nowcasting tool for high-frequency data, allowing to study the real-time response of covariances to macro-news announcements and to build intraday portfolios with very short optimization horizons.

Suggested Citation

  • Giuseppe Buccheri & Giacomo Bormetti & Fulvio Corsi & Fabrizio Lillo, 2021. "A Score-Driven Conditional Correlation Model for Noisy and Asynchronous Data: An Application to High-Frequency Covariance Dynamics," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(4), pages 920-936, October.
    • Handle: RePEc:taf:jnlbes:v:39:y:2021:i:4:p:920-936
    DOI: 10.1080/07350015.2020.1739530
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    Cited by:

    1. Jean-Claude Hessing & Rutger-Jan Lange & Daniel Ralph, 2022. "This article establishes the Poisson optional stopping times (POST) method by Lange et al. (2020) as a near-universal method for solving liquidity-constrained American options, or, equivalently, penal," Tinbergen Institute Discussion Papers 22-007/IV, Tinbergen Institute.
    2. Eric A. Beutner & Yicong Lin & Andre Lucas, 2023. "Consistency, distributional convergence, and optimality of score-driven filters," Tinbergen Institute Discussion Papers 23-051/III, Tinbergen Institute.
    3. D’Innocenzo, Enzo & Lucas, Andre, 2024. "Dynamic partial correlation models," Journal of Econometrics, Elsevier, vol. 241(2).
    4. Raanju R. Sundararajan & Wagner Barreto‐Souza, 2023. "Student‐t stochastic volatility model with composite likelihood EM‐algorithm," Journal of Time Series Analysis, Wiley Blackwell, vol. 44(1), pages 125-147, January.
    5. 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.
    6. Shen, Yiwen & Shi, Meiqi, 2024. "Intraday variation in cross-sectional stock comovement and impact of index-based strategies," Journal of Financial Markets, Elsevier, vol. 68(C).
    7. Vladim'ir Hol'y, 2022. "An Intraday GARCH Model for Discrete Price Changes and Irregularly Spaced Observations," Papers 2211.12376, arXiv.org, revised May 2024.
    8. Algaba, Andres & Borms, Samuel & Boudt, Kris & Verbeken, Brecht, 2023. "Daily news sentiment and monthly surveys: A mixed-frequency dynamic factor model for nowcasting consumer confidence," International Journal of Forecasting, Elsevier, vol. 39(1), pages 266-278.
    9. Leonardo Ieracitano Vieira & Márcio Poletti Laurini, 2023. "Time-varying higher moments in Bitcoin," Digital Finance, Springer, vol. 5(2), pages 231-260, June.
    10. 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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