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An Iterative Approach to Ill-Conditioned Optimal Portfolio Selection

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Abstract

Covariance matrix of the asset returns plays an important role in the portfolio selection. A number of papers is focused on the case when the covariance matrix is positive definite. In this paper, we consider portfolio selection with a singular covariance matrix. We describe an iterative method based on a second order damped dynamical systems that solves the linear rank-deficient problem approximately. Since the solution is not unique, we suggest one numerical solution that can be chosen from the iterates that balances the size of portfolio and the risk. The numerical study confirms that the method has good convergence properties and gives a solution as good as or better than the constrained least norm Moore-Penrose solution. Finally, we complement our result with an empirical study where we analyze a portfolio with actual returns listed in S&P 500 index.

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  • Gulliksson, Mårten & Mazur, Stepan, 2019. "An Iterative Approach to Ill-Conditioned Optimal Portfolio Selection," Working Papers 2019:3, Örebro University, School of Business.
  • Handle: RePEc:hhs:oruesi:2019_003
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    References listed on IDEAS

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    Cited by:

    1. Alfelt, Gustav & Mazur, Stepan, 2020. "On the mean and variance of the estimated tangency portfolio weights for small samples," Working Papers 2020:8, Örebro University, School of Business.

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    More about this item

    Keywords

    Mean-variance portfolio; singular covariance matrix; linear ill-posed problems; second order damped dynamical systems;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory

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