A Dynamic Structure for High Dimensional Covariance Matrices and its Application in Portfolio Allocation

Estimation of high dimensional covariance matrices is an interesting and important research topic. In this thesis, we propose a dynamic structure and develop an estimation procedure for high dimensional covariance matrices. Simulation studies are conducted to demonstrate its performance when the sample size is finite. By exploring a financial application, an empirical study shows that portfolio allocation based on dynamic high dimensional covariance matrices can significantly outperform the market from 1995 to 2014. Our proposed method also outperforms portfolio allocation based on the sample covariance matrix and the portfolio allocation proposed in Fan, Fan, Lv (2008).