A generalized pivotal quantity approach to portfolio selection

Leung Ho Philip YU, Thomas MATHEW, Yuanyuan ZHU

Research output: Contribution to journalArticles

1 Citation (Scopus)

Abstract

The major problem of mean–variance portfolio optimization is parameter uncertainty. Many methods have been proposed to tackle this problem, including shrinkage methods, resampling techniques, and imposing constraints on the portfolio weights, etc. This paper suggests a new estimation method for mean–variance portfolio weights based on the concept of generalized pivotal quantity (GPQ) in the case when asset returns are multivariate normally distributed and serially independent. Both point and interval estimations of the portfolio weights are considered. Comparing with Markowitz's mean–variance model, resampling and shrinkage methods, we find that the proposed GPQ method typically yields the smallest mean-squared error for the point estimate of the portfolio weights and obtains a satisfactory coverage rate for their simultaneous confidence intervals. Finally, we apply the proposed methodology to address a portfolio rebalancing problem. Copyright © 2016 Informa UK Limited, trading as Taylor & Francis Group.
Original languageEnglish
Pages (from-to)1402-1420
JournalJournal of Applied Statistics
Volume44
Issue number8
Early online dateJul 2017
DOIs
Publication statusPublished - 2017

Citation

Yu, P. L. H., Mathew, T., & Zhu, Y. (2017). A generalized pivotal quantity approach to portfolio selection. Journal of Applied Statistics, 44(8), 1402-1420. doi: 10.1080/02664763.2016.1214241

Keywords

  • Generalized pivotal quantity
  • Portfolio selection
  • Generalized confidence intervals

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