Estimating dynamic copula dependence using intraday data

Großmaß, Lidan; Poon, Ser-Huang

Estimating dynamic copula dependence using intraday data

Dateien

Grossmass_0-302906.pdfGröße: 1.44 MBDownloads: 369

Datum

2015

Autor:innen

Großmaß, Lidan

Poon, Ser-Huang

Open Access-Veröffentlichung

Open Access Green

Sammlungen

Wirtschaftswissenschaften

Publikationstyp

Zeitschriftenartikel

Publikationsstatus

Published

Erschienen in

Studies in Nonlinear Dynamics & Econometrics. 2015, 19(4), pp. 501-529. ISSN 1081-1826. eISSN 1558-3708. Available under: doi: 10.1515/snde-2013-0123

Zusammenfassung

We estimate the dynamic daily dependence between assets by applying the Semiparametric Copula-Based Multivariate Dynamic (SCOMDY) model on intraday data. Using tick data of three stock returns of the period before and during the credit crisis, we find that our dependence estimator better captures the steep increase in dependence during the onset of the crisis as compared to other commonly used time-varying copula methods. Like other high-frequency estimators, we find that the dependence estimator exhibits long memory and forecast it using a HAR model. We show that for out-of-sample forecasts, our dependence estimator performs better than the constant estimator and other commonly used time-varying copula dependence estimators.

Fachgebiet (DDC)

330 Wirtschaft

Schlagwörter

copula, high frequency data, intraday dependence, time-varying dependence, value-at-risk

Zitieren

ISO 690

GROSSMASS, Lidan, Ser-Huang POON, 2015. Estimating dynamic copula dependence using intraday data. In: Studies in Nonlinear Dynamics & Econometrics. 2015, 19(4), pp. 501-529. ISSN 1081-1826. eISSN 1558-3708. Available under: doi: 10.1515/snde-2013-0123

BibTex

@article{Groma2015Estim-32914,
  year={2015},
  doi={10.1515/snde-2013-0123},
  title={Estimating dynamic copula dependence using intraday data},
  number={4},
  volume={19},
  issn={1081-1826},
  journal={Studies in Nonlinear Dynamics & Econometrics},
  pages={501--529},
  author={Großmaß, Lidan and Poon, Ser-Huang}
}

RDF

<rdf:RDF
    xmlns:dcterms="http://purl.org/dc/terms/"
    xmlns:dc="http://purl.org/dc/elements/1.1/"
    xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
    xmlns:bibo="http://purl.org/ontology/bibo/"
    xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#"
    xmlns:foaf="http://xmlns.com/foaf/0.1/"
    xmlns:void="http://rdfs.org/ns/void#"
    xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > 
  <rdf:Description rdf:about="https://kops.uni-konstanz.de/server/rdf/resource/123456789/32914">
    <dc:language>eng</dc:language>
    <dcterms:rights rdf:resource="https://rightsstatements.org/page/InC/1.0/"/>
    <dc:contributor>Poon, Ser-Huang</dc:contributor>
    <dcterms:issued>2015</dcterms:issued>
    <dc:creator>Poon, Ser-Huang</dc:creator>
    <dcterms:abstract xml:lang="eng">We estimate the dynamic daily dependence between assets by applying the Semiparametric Copula-Based Multivariate Dynamic (SCOMDY) model on intraday data. Using tick data of three stock returns of the period before and during the credit crisis, we find that our dependence estimator better captures the steep increase in dependence during the onset of the crisis as compared to other commonly used time-varying copula methods. Like other high-frequency estimators, we find that the dependence estimator exhibits long memory and forecast it using a HAR model. We show that for out-of-sample forecasts, our dependence estimator performs better than the constant estimator and other commonly used time-varying copula dependence estimators.</dcterms:abstract>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/46"/>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/32914/3/Grossmass_0-302906.pdf"/>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/46"/>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2016-02-09T16:13:01Z</dc:date>
    <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/32914"/>
    <dcterms:title>Estimating dynamic copula dependence using intraday data</dcterms:title>
    <dc:creator>Großmaß, Lidan</dc:creator>
    <dc:contributor>Großmaß, Lidan</dc:contributor>
    <dc:rights>terms-of-use</dc:rights>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2016-02-09T16:13:01Z</dcterms:available>
    <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/32914/3/Grossmass_0-302906.pdf"/>
  </rdf:Description>
</rdf:RDF>