File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Forecasting high-dimensional realized volatility matrices using a factor model

TitleForecasting high-dimensional realized volatility matrices using a factor model
Authors
KeywordsHigh-dimension
High-frequency
Realized covariance matrices
Factor model
Wishart distribution
Issue Date2018
PublisherRoutledge. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/14697688.asp
Citation
Quantitative Finance, 2018, p. 1-9 How to Cite?
AbstractModelling and forecasting covariance matrices of asset returns play a crucial role in many financial fields, such as portfolio allocation and asset pricing. The availability of high-frequency intraday data enables the modelling of the realized covariance matrix directly. However, most models in the literature suffer from the curse of dimensionality, i.e. the number of parameters needed increases at the rate of the square of the number of assets. To solve the problem, we propose a factor model with a diagonal Conditional Autoregressive Wishart model for the factor realized covariance matrices. Consequently, the positive definiteness of the estimated covariance matrix is ensured with the proposed model. Asymptotic theory is derived for the estimated parameters. In the extensive empirical analysis, we find that the number of parameters can be reduced significantly; to only about one-tenth of the benchmark model. Furthermore, the proposed model maintains a comparable performance with a benchmark vector autoregressive model for different forecast horizons.
Persistent Identifierhttp://hdl.handle.net/10722/259516
ISSN
2015 Impact Factor: 0.794
2015 SCImago Journal Rankings: 0.565

 

DC FieldValueLanguage
dc.contributor.authorShen, K-
dc.contributor.authorYao, JJ-
dc.contributor.authorLi, WK-
dc.date.accessioned2018-09-03T04:09:09Z-
dc.date.available2018-09-03T04:09:09Z-
dc.date.issued2018-
dc.identifier.citationQuantitative Finance, 2018, p. 1-9-
dc.identifier.issn1469-7688-
dc.identifier.urihttp://hdl.handle.net/10722/259516-
dc.description.abstractModelling and forecasting covariance matrices of asset returns play a crucial role in many financial fields, such as portfolio allocation and asset pricing. The availability of high-frequency intraday data enables the modelling of the realized covariance matrix directly. However, most models in the literature suffer from the curse of dimensionality, i.e. the number of parameters needed increases at the rate of the square of the number of assets. To solve the problem, we propose a factor model with a diagonal Conditional Autoregressive Wishart model for the factor realized covariance matrices. Consequently, the positive definiteness of the estimated covariance matrix is ensured with the proposed model. Asymptotic theory is derived for the estimated parameters. In the extensive empirical analysis, we find that the number of parameters can be reduced significantly; to only about one-tenth of the benchmark model. Furthermore, the proposed model maintains a comparable performance with a benchmark vector autoregressive model for different forecast horizons.-
dc.languageeng-
dc.publisherRoutledge. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/14697688.asp-
dc.relation.ispartofQuantitative Finance-
dc.rightsPreprint: This is an Author's Original Manuscript of an article published by Taylor & Francis Group in [JOURNAL TITLE] on [date of publication], available online: http://www.tandfonline.com/doi/abs/[Article DOI]. Postprint: This is an Accepted Manuscript of an article published by Taylor & Francis Group in [JOURNAL TITLE] on [date of publication], available online at: http://www.tandfonline.com/doi/abs/[Article DOI]. -
dc.subjectHigh-dimension-
dc.subjectHigh-frequency-
dc.subjectRealized covariance matrices-
dc.subjectFactor model-
dc.subjectWishart distribution-
dc.titleForecasting high-dimensional realized volatility matrices using a factor model-
dc.typeArticle-
dc.identifier.emailYao, JJ: jeffyao@hku.hk-
dc.identifier.emailLi, WK: hrntlwk@hkucc.hku.hk-
dc.identifier.authorityYao, JJ=rp01473-
dc.identifier.authorityLi, WK=rp00741-
dc.identifier.doi10.1080/14697688.2018.1473632-
dc.identifier.hkuros289688-
dc.identifier.spage1-
dc.identifier.epage9-
dc.publisher.placeUnited Kingdom-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats