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Modeling price clustering in high-frequency prices

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  • Vladimír Holý
  • Petra Tomanová

Abstract

The price clustering phenomenon manifesting itself as an increased occurrence of specific prices is widely observed and well documented for various financial instruments and markets. In the literature, however, it is rarely incorporated into price models. We consider that there are several types of agents trading only in specific multiples of the tick size resulting in an increased occurrence of these multiples in prices. For example, stocks on the NYSE and NASDAQ exchanges are traded with precision to one cent but multiples of five cents and ten cents occur much more often in prices. To capture this behavior, we propose a discrete price model based on a mixture of double Poisson distributions with dynamic volatility and dynamic proportions of agent types. The model is estimated by the maximum likelihood method. In an empirical study of DJIA stocks, we find that higher instantaneous volatility leads to weaker price clustering at the ultra-high frequency. This is in sharp contrast with results at low frequencies which show that daily realized volatility has a positive impact on price clustering.

Suggested Citation

  • Vladimír Holý & Petra Tomanová, 2022. "Modeling price clustering in high-frequency prices," Quantitative Finance, Taylor & Francis Journals, vol. 22(9), pages 1649-1663, September.
  • Handle: RePEc:taf:quantf:v:22:y:2022:i:9:p:1649-1663
    DOI: 10.1080/14697688.2022.2050285
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    Cited by:

    1. Ramon de Punder & Timo Dimitriadis & Rutger-Jan Lange, 2024. "Kullback-Leibler-based characterizations of score-driven updates," Papers 2408.02391, arXiv.org, revised Sep 2024.

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