Association Journal of CSIAM
Supervised by Ministry of Education of PRC
Sponsored by Xi'an Jiaotong University
ISSN 1005-3085  CN 61-1269/O1

Chinese Journal of Engineering Mathematics

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Geometric Characterization of Efficient Frontier of Optimal Portfolio with Unconditional Covariance Matrix

WANG Xiaoling1,2,   WANG Yuwen3,4,   LIU Guanqi4   

  1. 1. College of Finance, Heilongjiang University of Finance and Economics, Harbin 150025

    2. International College, Krirk University, Bangkok 10220

    3. Department of Mathematics and Physics Teaching and Research, Harbin Institute of Petroleum, Harbin, 150027

    4. School of Mathematical Sciences, Harbin Normal University, Harbin 150025
  • Received:2022-12-21 Accepted:2023-04-27 Online:2025-10-15 Published:2025-10-15
  • Contact: Y. Wang. E-mail address: wangyuwen1950@aliyun.com
  • Supported by:
    The National Natural Science Foundation of China (12101163); the Key Teaching Reform Entrusted Project of Heilongjiang Province (SJGZ20190031); the School-level Project of Heilongjiang University of Finance and Economics (Youth) (XJQN202559).

Abstract:

In this paper, by using the Moore-Penrose generalized inverse of the covariance matrix, for the mean-variance selection problem of any finite kinds of risk assets portfolio, the expression of the optimal strategies and the variance for the minimum variance portfolio under the given portfolio expected return are given. By proceeding the generalized inverse analysis, we further deduced the geometric characterization of the efficient frontier of the portfolio under two different circumstances, respectively. The conclusions not only include the results of the covariance matrix under the positive definite condition in the literature, but also include the classical results of the geometric characterization of the effective frontier of the portfolio when $n=2$ and the two risky assets are completely negatively correlated.

Key words: portfolio, mean-variance, effective frontier, covariance matrix, Moore-Penrose generalized inverse

CLC Number: