High-Dimensional Minimum Variance Portfolio Estimation Based on High-Frequency Data

31 Pages Posted: 1 Feb 2018 Last revised: 3 Jun 2019

See all articles by Tony Cai

Tony Cai

University of Pennsylvania - Statistics Department

Jianchang Hu

University of Wisconsin - Madison - Department of Statistics

Yingying Li

Hong Kong University of Science & Technology (HKUST), Dept of ISOM and Dept of Finance; Hong Kong University of Science & Technology (HKUST) - Department of Information Systems, Business Statistics and Operations Management; Hong Kong University of Science & Technology (HKUST) - Department of Finance

Xinghua Zheng

Hong Kong University of Science & Technology (HKUST) - Department of Information Systems, Business Statistics and Operations Management

Date Written: June 1, 2019

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

JEL Classification: C13, C55, C58, G11

Suggested Citation

Cai, Tony and Hu, Jianchang and Li, Yingying and Zheng, Xinghua, High-Dimensional Minimum Variance Portfolio Estimation Based on High-Frequency Data (June 1, 2019). Journal of Econometrics, Forthcoming. Available at SSRN: https://ssrn.com/abstract=3105815 or http://dx.doi.org/10.2139/ssrn.3105815

Tony Cai

University of Pennsylvania - Statistics Department ( email )

Wharton School
Philadelphia, PA 19104
United States

Jianchang Hu (Contact Author)

University of Wisconsin - Madison - Department of Statistics ( email )

United States

Yingying Li

Hong Kong University of Science & Technology (HKUST), Dept of ISOM and Dept of Finance ( email )

Clear Water Bay, Kowloon
Hong Kong

Hong Kong University of Science & Technology (HKUST) - Department of Information Systems, Business Statistics and Operations Management ( email )

Clear Water Bay
Kowloon
Hong Kong

Hong Kong University of Science & Technology (HKUST) - Department of Finance ( email )

Clear Water Bay, Kowloon
Hong Kong

Xinghua Zheng

Hong Kong University of Science & Technology (HKUST) - Department of Information Systems, Business Statistics and Operations Management ( email )

Clear Water Bay
Kowloon
Hong Kong

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