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Article: On a spiked model for large volatility matrix estimation from noisy high-frequency data

TitleOn a spiked model for large volatility matrix estimation from noisy high-frequency data
Authors
KeywordsIntegrated covariance matrix
Pre-averaging
Random matrix theory
Spiked covariance matrix
Issue Date2018
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/csda
Citation
Computational Statistics & Data Analysis, 2018 How to Cite?
AbstractRecently, inference about high-dimensional integrated covariance matrices (ICVs) based on noisy high-frequency data has emerged as a challenging problem. In the literature, a pre-averaging estimator (PA-RCov) is proposed to deal with the microstructure noise. Using the large-dimensional random matrix theory, it has been established that the eigenvalue distribution of the PA-RCov matrix is intimately linked to that of the ICV through the Marčenko–Pasturequation. Consequently, the spectrum of the ICV can be inferred from that of the PA-RCov. However, extensive data analyses demonstrate that the spectrum of the PA-RCov is spiked, that is, a few large eigenvalues (spikes) stay away from the others which form a rather continuous distribution with a density function (bulk). Therefore, any inference on the ICVs must take into account this spiked structure. As a methodological contribution, a spiked model is proposed for the ICVs where spikes can be inferred from those of the available PA-RCov matrices. The consistency of the inference procedure is established. In addition, the methodology is applied to the real data from the US and Hong Kong markets. It is found that the model clearly outperforms the existing one in predicting the existence of spikes and in mimicking the empirical PA-RCov matrices.
Persistent Identifierhttp://hdl.handle.net/10722/259517
ISSN
2015 Impact Factor: 1.179
2015 SCImago Journal Rankings: 1.283

 

DC FieldValueLanguage
dc.contributor.authorShen, K-
dc.contributor.authorYao, JJ-
dc.contributor.authorLi, WK-
dc.date.accessioned2018-09-03T04:09:10Z-
dc.date.available2018-09-03T04:09:10Z-
dc.date.issued2018-
dc.identifier.citationComputational Statistics & Data Analysis, 2018-
dc.identifier.issn0167-9473-
dc.identifier.urihttp://hdl.handle.net/10722/259517-
dc.description.abstractRecently, inference about high-dimensional integrated covariance matrices (ICVs) based on noisy high-frequency data has emerged as a challenging problem. In the literature, a pre-averaging estimator (PA-RCov) is proposed to deal with the microstructure noise. Using the large-dimensional random matrix theory, it has been established that the eigenvalue distribution of the PA-RCov matrix is intimately linked to that of the ICV through the Marčenko–Pasturequation. Consequently, the spectrum of the ICV can be inferred from that of the PA-RCov. However, extensive data analyses demonstrate that the spectrum of the PA-RCov is spiked, that is, a few large eigenvalues (spikes) stay away from the others which form a rather continuous distribution with a density function (bulk). Therefore, any inference on the ICVs must take into account this spiked structure. As a methodological contribution, a spiked model is proposed for the ICVs where spikes can be inferred from those of the available PA-RCov matrices. The consistency of the inference procedure is established. In addition, the methodology is applied to the real data from the US and Hong Kong markets. It is found that the model clearly outperforms the existing one in predicting the existence of spikes and in mimicking the empirical PA-RCov matrices.-
dc.languageeng-
dc.publisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/csda-
dc.relation.ispartofComputational Statistics & Data Analysis-
dc.subjectIntegrated covariance matrix-
dc.subjectPre-averaging-
dc.subjectRandom matrix theory-
dc.subjectSpiked covariance matrix-
dc.titleOn a spiked model for large volatility matrix estimation from noisy high-frequency data-
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.doi10.1016/j.csda.2018.06.004-
dc.identifier.hkuros289690-
dc.publisher.placeNetherlands-

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