Shrinking the Variance-Covariance Matrix: Simpler is Better

Authors

  • Muhammad Husnain Department of Management Sciences, Capital University of Science and Technology, Islamabad; research fellow, IAE de Paris, Université Paris 1 Panthéon-Sorbonne.
  • Arshad Hassan Associate Professor, Faculty of Management and Social Sciences, Capital University of Science and Technology, Islamabad.
  • Eric Lamarque Professor, IAE de Paris, Université Paris 1 Panthéon-Sorbonne.

Keywords:

Variance-covariance matrix, mean-variance criteria, portfolio management.

Abstract

This study focuses on the estimation of the covariance matrix as an input to portfolio optimization. We compare 12 covariance estimators across four categories – conventional methods, factor models, portfolios of estimators and the shrinkage approach – applied to five emerging Asian economies (India, Indonesia, Pakistan, the Philippines and Thailand). We find that, in terms of the root mean square error and risk profile of minimum variance portfolios, investors gain no additional benefit from using the more complex shrinkage covariance estimators over the simpler, equally weighted portfolio of estimators in the sample countries.

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Published

2024-07-01