Journal of Asset Management

, Volume 7, Issue 5, pp 302–311

Mean–variance versus full-scale optimisation: In and out of sample


DOI: 10.1057/palgrave.jam.2250042

Cite this article as:
Adler, T. & Kritzman, M. J Asset Manag (2007) 7: 302. doi:10.1057/palgrave.jam.2250042


We present a recent innovation to portfolio construction called full-scale optimisation. In contrast to mean–variance analysis, which assumes that returns are normally distributed or that investors have quadratic utility, full-scale optimisation identifies the optimal portfolio given any set of return distributions and any description of investor preferences. It therefore yields the truly optimal portfolio in sample, whereas mean–variance analysis provides an approximation to the in-sample truth. Both approaches, however, suffer from estimation error. We employ a bootstrapping procedure to compare the estimation error of full-scale optimisation to the combined approximation and estimation error of mean–variance analysis. We find that, to a significant degree, the in-sample superiority of full-scale optimisation prevails out of sample.


full-scale mean variance optimization estimation error approximation error 
Download to read the full article text

Copyright information

© Palgrave Macmillan Ltd 2006

Authors and Affiliations

  1. 1.Windham Capital Management, LLCCambridgeUSA