Journal of Banking & Finance Vol. 36 No. 9 2012
Parameter uncertainty in portfolio selection: Shrinking the inverse covariance matrix
Abstract
The estimation of the inverse covariance matrix plays a crucial role in optimal portfolio choice. We propose a new estimation framework that focuses on enhancing portfolio performance. The framework applies the statistical methodology of shrinkage directly to the inverse covariance matrix using two non-parametric methods. The first minimises the out-of-sample portfolio variance while the second aims to increase out-of-sample risk-adjusted returns. We apply the resulting estimators to compute the minimum variance portfolio weights and obtain a set of new portfolio strategies. These strategies have an intuitive form which allows us to extend our framework to account for short-sale constraints, transaction costs and singular covariance matrices. A comparative empirical analysis against several strategies from the literature shows that the new strategies often offer higher risk-adjusted returns and lower levels of risk.
- DOI
- 10.1016/j.jbankfin.2012.05.005
- Volume
- 36
- Issue
- 9
- Pages
- 2522-2531
- Language
- en
- Sources
- openalex crossref bibtex:phds-export.bib