Parameter uncertainty in portfolio selection: Shrinking the inverse covariance matrix
- Resource Type
- Authors
- George Dotsis; Raphael N. Markellos; Apostolos Kourtis
- Source
- Journal of Banking & Finance. 36:2522-2531
- Subject
- Transaction cost
Set (abstract data type)
Economics and Econometrics
Mathematical optimization
Computer science
Estimator
Portfolio
Inverse covariance matrix
Variance (accounting)
Covariance
Finance
Selection (genetic algorithm)
- Language
- ISSN
- 0378-4266
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.