Title: High-dimensional minimum variance portfolio estimation based on high-frequency data
Authors: Tony Cai - University of Pennsylvania (United States)
Jianchang Hu - University of Wisconsin Madison (United States)
Yingying Li - Hong Kong University of Science and Technology (Hong Kong) [presenting]
Xinghua Zheng - HKUST (China)
Abstract: The aim is to study 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.