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High-dimensional minimum variance portfolio estimation based on high-frequency data

T. Tony Cai, Jianchang Hu, Yingying Li and Xinghua Zheng

Journal of Econometrics, 2020, vol. 214, issue 2, 482-494

Abstract: This paper studies the estimation of high-dimensional minimum variance portfolio (MVP) based on the high frequency returns which can exhibit heteroscedasticity and possibly be contaminated by microstructure noise. Under certain sparsity assumptions on the precision matrix, we propose estimators of the MVP and prove that our portfolios asymptotically achieve 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 the favorable performance of the proposed portfolios.

Keywords: Minimum variance portfolio; High dimension; High frequency; CLIME estimator; Precision matrix (search for similar items in EconPapers)
JEL-codes: C13 C55 C58 G11 (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1016/j.jeconom.2019.04.039

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Journal of Econometrics is currently edited by T. Amemiya, A. R. Gallant, J. F. Geweke, C. Hsiao and P. M. Robinson

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Handle: RePEc:eee:econom:v:214:y:2020:i:2:p:482-494