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

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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.

Original languageEnglish
JournalJournal of Multivariate Analysis
Pages (from-to)110-129
Number of pages20
Publication statusPublished - Jan 2019
Externally publishedYes

    Research areas

  • Continuous time modeling of multivariate time series, Count data, Infinitely divisible, Limit order book, Multivariate negative binomial law, Poisson mixtures, Trawl processes

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