Publication type: Article in scientific journal
Type of review: Peer review (publication)
Title: Dependence structures for multivariate high-frequency data in finance
Authors: Breymann, Wolfgang
Dias, Alexandra
Embrechts, Paul
DOI: 10.1080/713666155
Published in: Quantitative Finance
Volume(Issue): 3
Issue: 1
Page(s): 1
Pages to: 14
Issue Date: 2003
Publisher / Ed. Institution: Routledge
ISSN: 1469-7688
Language: English
Subject (DDC): 332: Financial economics
Abstract: Stylized facts for univariate high-frequency data in finance are well known. They include scaling behaviour, volatility clustering, heavy tails and seasonalities. The multivariate problem, however, has scarcely been addressed up to now. In this paper, bivariate series of high-frequency FX spot data for major FX markets are investigated. First, as an indispensable prerequisite for further analysis, the problem of simultaneous deseasonalization of high-frequency data is addressed. In the following sections we analyse in detail the dependence structure as a function of the timescale. Particular emphasis is put on the tail behaviour, which is investigated by means of copulas.
Fulltext version: Published version
License (according to publishing contract): Licence according to publishing contract
Departement: School of Engineering
Organisational Unit: Institute of Data Analysis and Process Design (IDP)
Appears in collections:Publikationen School of Engineering

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