Modeling, simulation and inference for multivariate time series of counts using trawl processes

Almut Veraart

Journal of Multivariate Analysis, 2019, vol. 169, issue C, 110-129

Abstract: This article presents a new continuous-time modeling framework for multivariate time series of counts which have an infinitely divisible marginal distribution. The model is based on a mixed moving average process driven by Lévy noise, called a trawl process, where the serial correlation and the cross-sectional dependence are modeled independently of each other. Such processes can exhibit short or long memory. We derive a stochastic simulation algorithm and a statistical inference method for such processes. The new methodology is then applied to high frequency financial data, where we investigate the relationship between the number of limit order submissions and deletions in a limit order book.

Keywords: Count data; Continuous time modeling of multivariate time series; Infinitely divisible; Limit order book; Multivariate negative binomial law; Poisson mixtures; Trawl processes (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:jmvana:v:169:y:2019:i:c:p:110-129

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DOI: 10.1016/j.jmva.2018.08.012

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