A Class of Structured High-Dimensional Dynamic Covariance Matrices

High-dimensional covariance matrices have attracted much attention of statisticians and econometricians during the past decades. Vast literature is devoted to the research in high-dimensional covariance matrices. However, most of them are for constant covariance matrices. In many applications, constant covariance matrices are not appropriate, e.g., in portfolio allocation, dynamic covariance matrices would make much more sense. Simply assuming each entry of a covariance matrix is a function of time to introduce a dynamic structure would not work. In this paper, we are going to introduce a class of high-dimensional dynamic covariance matrices in which a kind of additive structure is embedded. We will show the proposed high-dimensional dynamic covariance matrices have many advantages in applications. An estimation procedure is also proposed to estimate the proposed high-dimensional dynamic covariance matrices. Asymptotic properties are built to justify the proposed estimation procedure. Intensive simulation studies show the proposed estimation procedure works very well when sample size is finite. Finally, we apply the proposed high-dimensional dynamic covariance matrices, together with the proposed estimation procedure, to portfolio allocation. The results look very interesting. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may
apply 2023