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

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