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- Publisher Website: 10.1080/14697688.2018.1473632
- Scopus: eid_2-s2.0-85048076325
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Article: Forecasting high-dimensional realized volatility matrices using a factor model
Title | Forecasting high-dimensional realized volatility matrices using a factor model |
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Authors | |
Keywords | High-dimension High-frequency Realized covariance matrices Factor model Wishart distribution |
Issue Date | 2018 |
Publisher | Routledge. 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? |
Abstract | Modelling 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 Identifier | http://hdl.handle.net/10722/259516 |
ISSN | 2023 Impact Factor: 1.5 2023 SCImago Journal Rankings: 0.705 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Shen, K | - |
dc.contributor.author | Yao, JJ | - |
dc.contributor.author | Li, WK | - |
dc.date.accessioned | 2018-09-03T04:09:09Z | - |
dc.date.available | 2018-09-03T04:09:09Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | Quantitative Finance, 2018, p. 1-9 | - |
dc.identifier.issn | 1469-7688 | - |
dc.identifier.uri | http://hdl.handle.net/10722/259516 | - |
dc.description.abstract | Modelling 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.language | eng | - |
dc.publisher | Routledge. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/14697688.asp | - |
dc.relation.ispartof | Quantitative Finance | - |
dc.rights | Preprint: 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.subject | High-dimension | - |
dc.subject | High-frequency | - |
dc.subject | Realized covariance matrices | - |
dc.subject | Factor model | - |
dc.subject | Wishart distribution | - |
dc.title | Forecasting high-dimensional realized volatility matrices using a factor model | - |
dc.type | Article | - |
dc.identifier.email | Yao, JJ: jeffyao@hku.hk | - |
dc.identifier.email | Li, WK: hrntlwk@hkucc.hku.hk | - |
dc.identifier.authority | Yao, JJ=rp01473 | - |
dc.identifier.authority | Li, WK=rp00741 | - |
dc.identifier.doi | 10.1080/14697688.2018.1473632 | - |
dc.identifier.scopus | eid_2-s2.0-85048076325 | - |
dc.identifier.hkuros | 289688 | - |
dc.identifier.spage | 1 | - |
dc.identifier.epage | 9 | - |
dc.identifier.isi | WOS:000575952400010 | - |
dc.publisher.place | United Kingdom | - |
dc.identifier.issnl | 1469-7688 | - |