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

File Description SizeFormat 
Veraart_article_final.pdfFile embargoed until 05 September 2019366.98 kBAdobe PDF    Request a copy
Veraart_supplement_final.pdfFile embargoed until 05 September 2019659.36 kBAdobe PDF    Request a copy
Title: Modeling, simulation and inference for multivariate time series of counts using trawl processes
Authors: Veraart, A
Item Type: Journal Article
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.
Issue Date: 1-Jan-2019
Date of Acceptance: 24-Aug-2018
ISSN: 0047-259X
Publisher: Elsevier
Start Page: 110
End Page: 129
Journal / Book Title: Journal of Multivariate Analysis
Volume: 169
Copyright Statement: © 2018 Elsevier Inc. All rights reserved. This manuscript is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence
Sponsor/Funder: Commission of the European Communities
Funder's Grant Number: FP7-PEOPLE-2012-CIG-321707
Keywords: 0104 Statistics
Statistics & Probability
Publication Status: Published
Embargo Date: 2019-09-05
Online Publication Date: 2018-09-05
Appears in Collections:Mathematics
Faculty of Natural Sciences

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Creative Commonsx