Resampling vs. Shrinkage for Benchmarked Managers

Michael Wolf

No 263, IEW - Working Papers from Institute for Empirical Research in Economics - University of Zurich

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.

Keywords: Covariance matrix; Markowitz optimization; Resampling; Shrinkage; Tracking error (search for similar items in EconPapers)
JEL-codes: C13 C51 C61 G11 G15 (search for similar items in EconPapers)
Date: 2006-01
New Economics Papers: this item is included in nep-ecm
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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