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Resampling vs. Shrinkage for Benchmarked Managers

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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 eciency 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.

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Bibliographic Info

Paper provided by Institute for Empirical Research in Economics - University of Zurich in its series IEW - Working Papers with number 263.

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Date of creation: Jan 2006
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Handle: RePEc:zur:iewwpx:263

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Keywords: Covariance matrix; Markowitz optimization; Resampling; Shrinkage; Tracking error;

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  1. Olivier Ledoit & Michael Wolf, 2003. "Honey, I Shrunk the Sample Covariance Matrix," Working Papers 92, Barcelona Graduate School of Economics.
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Cited by:
  1. José Luis Barros Fernandes & José Renato Haas Ornelas & Oscar Augusto Martínez Cusicanqui, 2011. "Combining equilibrium, resampling, and analysts' views in portfolio optimization," BIS Papers chapters, in: Bank for International Settlements (ed.), Portfolio and risk management for central banks and sovereign wealth funds, volume 58, pages 75-84 Bank for International Settlements.
  2. Andras Niedermayer & Daniel Niedermayer, 2007. "Applying Markowitz's Critical Line Algorithm," Diskussionsschriften dp0701, Universitaet Bern, Departement Volkswirtschaft.

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