Please use this identifier to cite or link to this item:
Title: Dependence structures for multivariate high-frequency data in finance
Authors: Breymann, W.
Dias, Alexandra
Embrechts, P.
First Published: Feb-2003
Publisher: Taylor & Francis (Routledge): SSH Titles
Citation: Quantitative Finance, 2003, 3 (1), pp. 1-14
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.
DOI Link: 10.1088/1469-7688/3/1/301
ISSN: 1469-7688
eISSN: 1469-7696
Version: Post-print
Status: Peer-reviewed
Type: Journal Article
Rights: Copyright © 2003, Taylor and Francis. The file associated with this record is distributed under the Creative Commons “Attribution Non-Commercial No Derivatives” licence, further details of which can be found via the following link:
Appears in Collections:Published Articles, School of Management

Files in This Item:
File Description SizeFormat 
qmf2002.pdfPost-review (final submitted)532.18 kBAdobe PDFView/Open

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