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A New Semiparametric Estimation Approach for Large Dynamic Covariance Matrices with Multiple Conditioning Variables

Jia Chen (), Degui Li and Oliver Linton ()

Cambridge Working Papers in Economics from Faculty of Economics, University of Cambridge

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.

Keywords: Dynamic covariance matrix; MAMAR; Semiparametric estimation; Sparsity; Uniform consistency (search for similar items in EconPapers)
JEL-codes: C10 C13 C14 G11 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ore
Date: 2018-10-24
Note: obl20
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Working Paper: A New Semiparametric Estimation Approach for Large Dynamic Covariance Matrices with Multiple Conditioning Variables (2018) Downloads
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