Portfolio Optimisation via Graphical Least Squares Estimation

Saeed Aldahmani, Hongsheng Dai, Qiao Zhen Zhang, Marialuisa Restaino

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In this paper, an unbiased estimation method called GLSE (proposed by Aldahmani and Dai[1]) for solving the linear regression problem in high-dimensional data is applied to portfolio optimisation under the linear regression framework and compared to the ridge method. The unbiasedness of method helps in improving the portfolio performance by increasing its expected return and decreasing the associated risk when, thus leading to a maximisation of the Sharpe ratio. The verification of this method is achieved through conducting simulation and data analysis studies and comparing the results with those of ridge regression. It is found that GLSE outperforms ridge in portfolio optimisation when.

Original languageEnglish
Title of host publicationNonparametric Statistics - 4th ISNPS 2018
EditorsMichele La Rocca, Brunero Liseo, Luigi Salmaso
Number of pages9
ISBN (Print)9783030573058
Publication statusPublished - 2020
Event4th Conference of the International Society for Nonparametric Statistics, ISNPS 2018 - Salerno, Italy
Duration: Jun 11 2018Jun 15 2018

Publication series

NameSpringer Proceedings in Mathematics and Statistics
ISSN (Print)2194-1009
ISSN (Electronic)2194-1017


Conference4th Conference of the International Society for Nonparametric Statistics, ISNPS 2018


  • Graphical model
  • Linear regression
  • Ridge regression

ASJC Scopus subject areas

  • General Mathematics


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