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Dynamic Discrete Mixtures for High Frequency Prices

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Dynamic Discrete Mixtures for High Frequency Prices. / Catania, Leopoldo; Di Mari, Roberto; Santucci de Magistris, Paolo.
In: Journal of Business and Economic Statistics, Vol. 40, No. 2, 2022, p. 559-577.

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaper › Journal article › Research › peer-review

Harvard

Catania, L, Di Mari, R & Santucci de Magistris, P 2022, 'Dynamic Discrete Mixtures for High Frequency Prices', Journal of Business and Economic Statistics, vol. 40, no. 2, pp. 559-577. https://doi.org/10.1080/07350015.2020.1840994

APA

Catania, L., Di Mari, R., & Santucci de Magistris, P. (2022). Dynamic Discrete Mixtures for High Frequency Prices. Journal of Business and Economic Statistics, 40(2), 559-577. Advance online publication. https://doi.org/10.1080/07350015.2020.1840994

CBE

Catania L, Di Mari R, Santucci de Magistris P. 2022. Dynamic Discrete Mixtures for High Frequency Prices. Journal of Business and Economic Statistics. 40(2):559-577. https://doi.org/10.1080/07350015.2020.1840994

MLA

Catania, Leopoldo, Roberto Di Mari and Paolo Santucci de Magistris. "Dynamic Discrete Mixtures for High Frequency Prices". Journal of Business and Economic Statistics. 2022, 40(2). 559-577. https://doi.org/10.1080/07350015.2020.1840994

Vancouver

Catania L, Di Mari R, Santucci de Magistris P. Dynamic Discrete Mixtures for High Frequency Prices. Journal of Business and Economic Statistics. 2022;40(2):559-577. Epub 2020 Dec 1. doi: 10.1080/07350015.2020.1840994

Author

Catania, Leopoldo ; Di Mari, Roberto ; Santucci de Magistris, Paolo. / Dynamic Discrete Mixtures for High Frequency Prices. In: Journal of Business and Economic Statistics. 2022 ; Vol. 40, No. 2. pp. 559-577.

Bibtex

@article{5c1d8d1e7f7b49bc8378f54ed286ba20,
title = "Dynamic Discrete Mixtures for High Frequency Prices",
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.",
keywords = "Dynamic mixtures, EM Algorithm, High-frequency prices, Skellam distribution, Volatility, Zeros",
author = "Leopoldo Catania and {Di Mari}, Roberto and {Santucci de Magistris}, Paolo",
year = "2022",
doi = "10.1080/07350015.2020.1840994",
language = "English",
volume = "40",
pages = "559--577",
journal = "Journal of Business and Economic Statistics",
issn = "0735-0015",
publisher = "Taylor & Francis Inc.",
number = "2",

}

RIS

TY - JOUR

T1 - Dynamic Discrete Mixtures for High Frequency Prices

AU - Catania, Leopoldo

AU - Di Mari, Roberto

AU - Santucci de Magistris, Paolo

PY - 2022

Y1 - 2022

N2 - 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.

AB - 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.

KW - Dynamic mixtures

KW - EM Algorithm

KW - High-frequency prices

KW - Skellam distribution

KW - Volatility

KW - Zeros

U2 - 10.1080/07350015.2020.1840994

DO - 10.1080/07350015.2020.1840994

M3 - Journal article

VL - 40

SP - 559

EP - 577

JO - Journal of Business and Economic Statistics

JF - Journal of Business and Economic Statistics

SN - 0735-0015

IS - 2

ER -