Nonparametric Estimation of Large Covariance Matrices with Conditional Sparsity

• DOI: 10.2139/ssrn.3515624
• Publication Date: 2020-01-31T06:31:56Z
• Authors: Hanchao Wang; Bin Peng; Degui Li; Chenlei Leng
• Keywords: 0502 economics and business; 05 social sciences; 0101 mathematics; QA; 01 natural sciences

This paper studies estimation of covariance matrices with conditional sparse structure. We overcome the challenge estimating dense using a factor structure, large-dimensional by postulating sparsity on random noises, and varying allowing loadings to smoothly change. A kernel-weighted approach combined generalised shrinkage is proposed. Under mild conditions, we derive uniform consistency for developed method obtain convergence rates. Numerical including simulation an empirical application are presented examine finite-sample performance methodology.