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

TY - JOUR

T1 - A new semiparametric estimation approach for large dynamic covariance matrices with multiple conditioning variables

AU - Chen, Jia

AU - Li, Degui

AU - Linton, Oliver

N1 - © 2019 Elsevier B.V. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy.

PY - 2019/9/1

Y1 - 2019/9/1

N2 - 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.

AB - 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.

KW - Dynamic covariance matrix

KW - MAMAR

KW - Semiparametric estimation

KW - Sparsity

KW - Uniform consistency

UR - http://www.scopus.com/inward/record.url?scp=85065078802&partnerID=8YFLogxK

U2 - 10.1016/j.jeconom.2019.04.025

DO - 10.1016/j.jeconom.2019.04.025

M3 - Article

SN - 0304-4076

VL - 212

SP - 155

EP - 176

JO - Journal of Econometrics

JF - Journal of Econometrics

IS - 1

ER -