A New Semiparametric Estimation Approach of Large Dynamic Covariance Matrices with Multiple Conditioning Variables

浙江大学数学科学学院九十周年院庆系列活动之四十四

题目：A New Semiparametric Estimation Approach of Large Dynamic Covariance Matrices with Multiple Conditioning Variables

时间：7月6日（周一）上午9:00―10:00

地点：工商楼2楼报告厅（200-9）

报告人：Professor Jia Chen（University of York, UK）

摘要：We study estimation of dynamic covariance matrices with multiple conditioning variables, where the matrix size can be ultra large (divergent at an exponential rate of the sample size). 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 large 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. Simulation studies are conducted to illustrate the finite-sample performance of the developed methodology. An application to financial portfolio choice is also provided.

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联系人：张荣茂教授（rmzhang@zju.edu.cn)