High-dimensional Minimum Variance Portfolio Estimation Based on High-frequency Data
Tony Cai, Jianchang Hu, Yingying Li, and Xinhua Zheng
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Abstract:
This paper studies the estimation of high-dimensional minimum variance portfolio (MVP) based on high frequency returns which can exhibit heteroskedasticity and possibly be contaminated by microstructure noise. Under certain sparsity assumptions on the precision matrix, we propose an estimator of MVP and prove that our portfolio asymptotically achieves the minimum variance in a sharp sense. In addition, we introduce consistent estimators of the minimum variance, which provide reference targets. Simulation and empirical studies demonstrate that our proposed portfolio performs favorably
- Paper: pdf file.