Modeling price clustering in high-frequency prices

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.

Keywords:

• High-frequency data
• Price clustering
• Generalized autoregressive score model
• Double Poisson distribution

JEL Classifications:

• C22
• C46
• C58

Acknowledgments

Computational resources were supplied by the project ‘e-Infrastruktura CZ’ (e-INFRA LM2018140) provided within the program Projects of Large Research, Development and Innovations Infrastructures.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 The RTX company results from the merge of the United Technologies Corporation and the Raytheon Company on April 3, 2020.

2 Time effects are dropped for better visibility which does not alter the main result.

Additional information

Funding

This research was supported by the Czech Science Foundation under project 19-02773S, the Internal Grant Agency of the Prague University of Economics and Business under project F4/53/2019, and the Institutional Support Funds for the long-term conceptual development of the Faculty of Informatics, Prague University of Economics and Business.