Parameter uncertainty in portfolio selection: Shrinking the inverse covariance matrix |
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Authors: | Apostolos Kourtis George Dotsis Raphael N Markellos |
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Institution: | 1. Norwich Business School, University of East Anglia, Norwich Research Park, Norwich NR4 7TJ, UK;2. Essex Business School, University of Essex, Wivenhoe Park, Colchester, Essex CO4 3SQ, UK;3. Department of Economics, University of Athens, Athens 10562, Greece |
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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. |
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Keywords: | C13 C51 C61 G11 |
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