A Dynamic Structure for High Dimensional Covariance Matrices and Its Application in Portfolio Allocation
The estimation of high dimensional covariance matrix is an important subject in statistics and econometrics. Most of the existing methods assume the covariance matrix is a constant matrix. This assumption limits the application of covariance matrix estimation. In many cases, the covariance matrix concerned is dynamic. In this talk, I am going to present a new type of dynamic covariance matrices. An estimation procedure of the proposed dynamic covariance matrices will be described in this talk. Intensive simulation studies are also conducted to show how well the proposed estimation methods work. Finally, I will show an example in which the proposed dynamic covariance matrices with the associated estimation procedure are used to allocate portfolio in an investment in stock market. The return of the portfolio constructed based our method seems very encouraging.
