Chen, Jia orcid.org/0000-0002-2791-2486, Li, Degui orcid.org/0000-0001-6802-308X and Linton, Oliver (2019) A new semiparametric estimation approach for large dynamic covariance matrices with multiple conditioning variables. Journal of Econometrics. pp. 155-176. ISSN 0304-4076
Abstract
This 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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 Elsevier B.V. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. |
Keywords: | Dynamic covariance matrix,MAMAR,Semiparametric estimation,Sparsity,Uniform consistency |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Mathematics (York) The University of York > Faculty of Social Sciences (York) > Economics and Related Studies (York) |
Depositing User: | Pure (York) |
Date Deposited: | 25 Oct 2018 12:30 |
Last Modified: | 26 Nov 2024 00:40 |
Published Version: | https://doi.org/10.1016/j.jeconom.2019.04.025 |
Status: | Published |
Refereed: | Yes |
Identification Number: | 10.1016/j.jeconom.2019.04.025 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:137792 |
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Licence: CC-BY-NC-ND 2.5