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A new semiparametric estimation approach for large dynamic covariance matrices with multiple conditioning variables

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Publication details

JournalJournal of Econometrics
DateAccepted/In press - 15 Oct 2018
DateE-pub ahead of print - 12 Apr 2019
DatePublished (current) - 1 Sep 2019
Issue number1
Number of pages22
Pages (from-to)155-176
Early online date12/04/19
Original languageEnglish


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.

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© 2019 Elsevier B.V. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy.

    Research areas

  • Dynamic covariance matrix, MAMAR, Semiparametric estimation, Sparsity, Uniform consistency

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