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

Jia Chen, Degui Li and Oliver Linton

Journal of Econometrics, 2019, vol. 212, issue 1, 155-176

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)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (18)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0304407619300806
Full text for ScienceDirect subscribers only

Related works:
Working Paper: A New Semiparametric Estimation Approach for Large Dynamic Covariance Matrices with Multiple Conditioning Variables (2018)
Working Paper: A New Semiparametric Estimation Approach for Large Dynamic Covariance Matrices with Multiple Conditioning Variables (2018)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:212:y:2019:i:1:p:155-176

DOI: 10.1016/j.jeconom.2019.04.025

Access Statistics for this article

Journal of Econometrics is currently edited by T. Amemiya, A. R. Gallant, J. F. Geweke, C. Hsiao and P. M. Robinson

More articles in Journal of Econometrics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().