Large Dynamic Covariance Matrix Estimation with an Application to Portfolio Allocation:A Semiparametric Reproducing Kernel Hilbert Space Approach  

作　　者：PENG Siyang GUO Shaojun LONG Yonghong 

机构地区：[1]School of Mathematics,Renmin University of China,Beijing,100086,China [2]Institute of Statistics and Big Data,Renmin University of China,Beijing,100086,China

出　　处：《Journal of Systems Science & Complexity》2022年第4期1429-1457,共29页系统科学与复杂性学报（英文版）

基　　金：supported by National Natural Science Foundation of China under Grant No.11771447。

摘　　要：The estimation of high dimensional covariance matrices is an interesting and important research topic for many empirical time series problems such as asset allocation. To solve this dimension dilemma, a factor structure has often been taken into account. This paper proposes a dynamic factor structure whose factor loadings are generated in reproducing kernel Hilbert space(RKHS), to capture the dynamic feature of the covariance matrix. A simulation study is carried out to demonstrate its performance. Four different conditional variance models are considered for checking the robustness of our method and solving the conditional heteroscedasticity in the empirical study. By exploring the performance among eight introduced model candidates and the market baseline, the empirical study from 2001 to 2017 shows that portfolio allocation based on this dynamic factor structure can significantly reduce the variance, i.e., the risk, of portfolio and thus outperform the market baseline and the ones based on the traditional factor model.

关 键 词：Dynamic structure factor models high dimensional covariance matrices portfolio allocation reproducing kernel Hilbert space 

分 类 号：F830[经济管理—金融学] O212.1[理学—概率论与数理统计]