Semiparametric model for covariance regression analysis

Estimating covariance matrices is an important research topic in statistics and finance. A semiparametric model for covariance matrix estimation is proposed. Specifically, the covariance matrix is modeled as a polynomial function of the symmetric adjacency matrix with time varying parameters. The asymptotic properties for the time varying coefficient and the associated semiparametric covariance estimators are established. A Bayesian information criterion to select the order of the polynomial function is also investigated. Simulation studies and an empirical example are presented to illustrate the usefulness of the proposed method. (C) 2019 Elsevier B.V. All rights reserved.