Forecasting Covariance Matrices : a Mixed Frequency Approach

Chiriac, Roxana; Voev, Valeri

Forecasting Covariance Matrices : a Mixed Frequency Approach

Dateien

Halbleib_0-274617.pdfGröße: 1.24 MBDownloads: 236

Datum

2012

Autor:innen

Chiriac, Roxana

Voev, Valeri

Open Access-Veröffentlichung

Open Access Green

Sammlungen

Wirtschaftswissenschaften

Publikationstyp

Working Paper/Technical Report

Publikationsstatus

Published

Zusammenfassung

In this paper we introduce a new method of forecasting covariance matrices of large dimensions by exploiting the theoretical and empirical potential of using mixed-frequency sampled data. The idea is to use high-frequency (intraday) data to model and forecast daily realized volatilities combined with low frequency (daily) data as input to the correlation model. The main theoretical contribution of the paper is to derive statistical and economic conditions, which ensure that a mixed-frequency forecast has a smaller mean squared forecast error than a similar pure low-frequency or pure high-frequency specification. The conditions are very general and do not rely on distributional assumptions of the forecasting errors or on a particular model specification. Moreover, we provide empirical evidence that, besides overcoming the computational burden of pure high-frequency specifications, the mixed-frequency forecasts are particularly useful in turbulent financial periods, such as the previous financial crisis and always outperforms the pure low-frequency specifications.

Fachgebiet (DDC)

330 Wirtschaft

Schlagwörter

Multivariate volatility, Volatility forecasting, High-frequency data, Realized variance, Realized covariance

Zitieren

ISO 690

CHIRIAC, Roxana, Valeri VOEV, 2012. Forecasting Covariance Matrices : a Mixed Frequency Approach

BibTex

@techreport{Chiriac2012Forec-29703,
  year={2012},
  series={Working Paper Series / Department of Economics},
  title={Forecasting Covariance Matrices : a Mixed Frequency Approach},
  number={2012‐30},
  author={Chiriac, Roxana and Voev, Valeri}
}

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/123456789/29703">
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/46"/>
    <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/29703/3/Halbleib_0-274617.pdf"/>
    <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/29703/3/Halbleib_0-274617.pdf"/>
    <dcterms:issued>2012</dcterms:issued>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/46"/>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2015-02-02T11:01:05Z</dcterms:available>
    <dc:rights>terms-of-use</dc:rights>
    <dc:creator>Voev, Valeri</dc:creator>
    <bibo:uri rdf:resource="http://kops.uni-konstanz.de/handle/123456789/29703"/>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dcterms:title>Forecasting Covariance Matrices : a Mixed Frequency Approach</dcterms:title>
    <dcterms:abstract xml:lang="eng">In this paper we introduce a new method of forecasting covariance matrices of large dimensions by exploiting the theoretical and empirical potential of using mixed-frequency sampled data. The idea is to use high-frequency (intraday) data to model and forecast daily realized volatilities combined with low frequency (daily) data as input to the correlation model. The main theoretical contribution of the paper is to derive statistical and economic conditions, which ensure that a mixed-frequency forecast has a smaller mean squared forecast error than a similar pure low-frequency or pure high-frequency specification. The conditions are very general and do not rely on distributional assumptions of the forecasting errors or on a particular model specification. Moreover, we provide empirical evidence that, besides overcoming the computational burden of pure high-frequency specifications, the mixed-frequency forecasts are particularly useful in turbulent financial periods, such as the previous financial crisis and always outperforms the pure low-frequency specifications.</dcterms:abstract>
    <dc:creator>Chiriac, Roxana</dc:creator>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2015-02-02T11:01:05Z</dc:date>
    <dcterms:rights rdf:resource="https://rightsstatements.org/page/InC/1.0/"/>
    <dc:contributor>Chiriac, Roxana</dc:contributor>
    <dc:language>eng</dc:language>
    <dc:contributor>Voev, Valeri</dc:contributor>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
  </rdf:Description>
</rdf:RDF>

Universitätsbibliographie

Ja