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Resampling vs. Shrinkage for Benchmarked Managers

Author

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  • Michael Wolf

Abstract

A well-known pitfall of Markowitz (1952) portfolio optimization is that the sample covariance matrix, which is a critical input, is very erroneous when there are many assets to choose from. If unchecked, this phenomenon skews the optimizer towards extreme weights that tend to perform poorly in the real world. One solution that has been proposed is to shrink the sample covariance matrix by pulling its most extreme elements towards more moderate values. An alternative solution is the resampled efficiency suggested by Michaud (1998). This paper compares shrinkage estimation to resampled efficiency. In addition, it studies whether the two techniques can be combined to achieve a further improvement. All this is done in the context of an active portfolio manager who aims to outperform a benchmark index and who is evaluated by his realized information ratio.

Suggested Citation

  • Michael Wolf, 2006. "Resampling vs. Shrinkage for Benchmarked Managers," IEW - Working Papers 263, Institute for Empirical Research in Economics - University of Zurich.
  • Handle: RePEc:zur:iewwpx:263
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    File URL: http://www.econ.uzh.ch/static/wp_iew/iewwp263.pdf
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    References listed on IDEAS

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    1. Olivier Ledoit & Michael Wolf, 2003. "Honey, I shrunk the sample covariance matrix," Economics Working Papers 691, Department of Economics and Business, Universitat Pompeu Fabra.
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    Cited by:

    1. Andras Niedermayer & Daniel Niedermayer, 2006. "Applying Markowitz's Critical Line Algorithm," Diskussionsschriften dp0602, Universitaet Bern, Departement Volkswirtschaft.
    2. Barros Fernandes, José Luiz & Haas Ornelas, José Renato & Martínez Cusicanqui, Oscar Augusto, 2012. "Combining equilibrium, resampling, and analyst’s views in portfolio optimization," Journal of Banking & Finance, Elsevier, vol. 36(5), pages 1354-1361.

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    More about this item

    Keywords

    Covariance matrix; Markowitz optimization; Resampling; Shrinkage; Tracking error;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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