Logo of Humboldt-Universität zu BerlinLogo of Humboldt-Universität zu Berlin
edoc-Server
Open-Access-Publikationsserver der Humboldt-Universität
de|en
Header image: facade of Humboldt-Universität zu Berlin
View Item 
  • edoc-Server Home
  • Schriftenreihen und Sammelbände
  • Fakultäten und Institute der HU
  • Wirtschaftswissenschaftliche Fakultät
  • Sonderforschungsbereich 649: Ökonomisches Risiko
  • View Item
  • edoc-Server Home
  • Schriftenreihen und Sammelbände
  • Fakultäten und Institute der HU
  • Wirtschaftswissenschaftliche Fakultät
  • Sonderforschungsbereich 649: Ökonomisches Risiko
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.
All of edoc-ServerCommunity & CollectionTitleAuthorSubjectThis CollectionTitleAuthorSubject
PublishLoginRegisterHelp
StatisticsView Usage Statistics
All of edoc-ServerCommunity & CollectionTitleAuthorSubjectThis CollectionTitleAuthorSubject
PublishLoginRegisterHelp
StatisticsView Usage Statistics
View Item 
  • edoc-Server Home
  • Schriftenreihen und Sammelbände
  • Fakultäten und Institute der HU
  • Wirtschaftswissenschaftliche Fakultät
  • Sonderforschungsbereich 649: Ökonomisches Risiko
  • View Item
  • edoc-Server Home
  • Schriftenreihen und Sammelbände
  • Fakultäten und Institute der HU
  • Wirtschaftswissenschaftliche Fakultät
  • Sonderforschungsbereich 649: Ökonomisches Risiko
  • View Item
2012-09-12Buch DOI: 10.18452/4428
Modeling Time-Varying Dependencies between Positive-Valued High-Frequency Time Series
Hautsch, Nikolaus
Okhrin, Ostap
Ristig, Alexander
Multiplicative error models (MEM) became a standard tool for modeling conditional durations of intraday transactions, realized volatilities and trading volumes. The parametric estimation of the corresponding multivariate model, the so-called vector MEM (VMEM), requires a specification of the joint error term distribution, which is due to the lack of multivariate distribution functions on Rd + defined via a copula. Maximum likelihood estimation is based on the assumption of constant copula parameters and therefore, leads to invalid inference, if the dependence exhibits time variations or structural breaks. Hence, we suggest to test for time-varying dependence by calibrating a time-varying copula model and to reestimate the VMEM based on identified intervals of homogenous dependence. This paper summarizes the important aspects of (V)MEM, its estimation and a sequential test for changes in the dependence structure. The techniques are applied in an empirical example.
Files in this item
Thumbnail
54.pdf — Adobe PDF — 2.275 Mb
MD5: 27e054126108742d9e18d6b1bdfd7e70
Cite
BibTeX
EndNote
RIS
InCopyright
Details
DINI-Zertifikat 2019OpenAIRE validatedORCID Consortium
Imprint Policy Contact Data Privacy Statement
A service of University Library and Computer and Media Service
© Humboldt-Universität zu Berlin
 
DOI
10.18452/4428
Permanent URL
https://doi.org/10.18452/4428
HTML
<a href="https://doi.org/10.18452/4428">https://doi.org/10.18452/4428</a>