Modelling, Simulation and Inference for Multivariate Time Series of Counts Using Trawl Processes

80 Pages Posted: 17 Jan 2018

See all articles by Almut Veraart

Almut Veraart

Imperial College London; CREATES

Date Written: January 11, 2018

Abstract

This article presents a new continuous-time modelling 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 Levy noise - called a trawl process - where the serial correlation and the cross-sectional dependence are modelled 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 modelling of multivariate time series, trawl processes, infinitely divisible, Poisson mixtures, multivariate negative binomial law, limit order book

JEL Classification: C32, C58

Suggested Citation

Veraart, Almut, Modelling, Simulation and Inference for Multivariate Time Series of Counts Using Trawl Processes (January 11, 2018). Available at SSRN: https://ssrn.com/abstract=3100076 or http://dx.doi.org/10.2139/ssrn.3100076

Almut Veraart (Contact Author)

Imperial College London ( email )

Department of Mathematics
180 Queen's Gate
London, SW7 2AZ

CREATES ( email )

Aarhus University
DK-8000 Aarhus C
Denmark

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