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Dynamic Discrete Mixtures for High-Frequency Prices

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  • Leopoldo Catania
  • Roberto Di Mari
  • Paolo Santucci de Magistris

Abstract

The tick structure of the financial markets entails discreteness of stock price changes. Based on this empirical evidence, we develop a multivariate model for discrete price changes featuring a mechanism to account for the large share of zero returns at high frequency. We assume that the observed price changes are independent conditional on the realization of two hidden Markov chains determining the dynamics and the distribution of the multivariate time series at hand. We study the properties of the model, which is a dynamic mixture of zero-inflated Skellam distributions. We develop an expectation-maximization algorithm with closed-form M-step that allows us to estimate the model by maximum likelihood. In the empirical application, we study the joint distribution of the price changes of a number of assets traded on NYSE. Particular focus is dedicated to the assessment of the quality of univariate and multivariate density forecasts, and of the precision of the predictions of moments like volatility and correlations. Finally, we look at the predictability of price staleness and its determinants in relation to the trading activity on the financial markets.

Suggested Citation

  • Leopoldo Catania & Roberto Di Mari & Paolo Santucci de Magistris, 2022. "Dynamic Discrete Mixtures for High-Frequency Prices," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(2), pages 559-577, April.
    • Handle: RePEc:taf:jnlbes:v:40:y:2022:i:2:p:559-577
    DOI: 10.1080/07350015.2020.1840994
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    Cited by:

    1. Maria Ludovica Drudi & Giulio Carlo Venturi, 2023. "Assessing the liquidity premium in the Italian bond market," Questioni di Economia e Finanza (Occasional Papers) 795, Bank of Italy, Economic Research and International Relations Area.
    2. Chengyu Li & Luyi Shen & Guoqi Qian, 2023. "Online Hybrid Neural Network for Stock Price Prediction: A Case Study of High-Frequency Stock Trading in the Chinese Market," Econometrics, MDPI, vol. 11(2), pages 1-19, May.
    3. Di Mari, Roberto & Bakk, Zsuzsa & Oser, Jennifer & Kuha, Jouni, 2023. "A two-step estimator for multilevel latent class analysis with covariates," LSE Research Online Documents on Economics 119994, London School of Economics and Political Science, LSE Library.
    4. 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.

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