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dc.contributor.authorChen, J.
dc.contributor.authorLi, D.
dc.contributor.authorLinton, O.
dc.date.accessioned2019-02-04T12:13:56Z
dc.date.available2019-02-04T12:13:56Z
dc.date.issued2018-10-24
dc.identifier.otherCWPE1876
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/288754
dc.description.abstractThis paper studies the estimation of large dynamic covariance matrices with multiple conditioning variables. We introduce an easy-to-implement semiparametric method to estimate each entry of the covariance matrix via model averaging marginal regression, and then apply a shrinkage technique to obtain the dynamic covariance matrix estimation. Under some regularity conditions, we derive the asymptotic properties for the proposed estimators including the uniform consistency with general convergence rates. We further consider extending our methodology to deal with the scenarios: (i) the number of conditioning variables is divergent as the sample size increases, and (ii) the large covariance matrix is conditionally sparse relative to contemporaneous market factors. We provide a simulation study that illustrates the finite-sample performance of the developed methodology. We also provide an application to financial portfolio choice from daily stock returns.
dc.publisherFaculty of Economics
dc.relation.ispartofseriesCambridge Working Papers in Economics
dc.rightsAll Rights Reserveden
dc.rights.urihttps://www.rioxx.net/licenses/all-rights-reserved/en
dc.subjectDynamic covariance matrix
dc.subjectMAMAR
dc.subjectSemiparametric estimation
dc.subjectSparsity
dc.subjectUniform consistency
dc.titleA New Semiparametric Estimation Approach for Large Dynamic Covariance Matrices with Multiple Conditioning Variables
dc.typeWorking Paper
dc.identifier.doi10.17863/CAM.36015


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