Mean-Variance Versus Full-Scale Optimization: In and Out of Sample

20 Pages Posted: 16 Feb 2006

See all articles by Timothy Adler

Timothy Adler

Windham Capital Management

Mark Kritzman

Massachusetts Institute of Technology (MIT) - Sloan School of Management

Abstract

We present a recent innovation to portfolio construction called full-scale optimization. In contrast to mean-variance analysis, which assumes that returns are normally distributed or that investors have quadratic utility, full-scale optimization 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 optimization 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 optimization prevails out-of-sample.

Keywords: Mean-Variance Analysis, Full-Scale Optimization, Portfolio Formation

Suggested Citation

Adler, Timothy and Kritzman, Mark, Mean-Variance Versus Full-Scale Optimization: In and Out of Sample . MIT Sloan Research Paper No. 4589-05. Available at SSRN: https://ssrn.com/abstract=881813

Timothy Adler (Contact Author)

Windham Capital Management ( email )

5 Revere Street
Cambridge, MA 02138
United States
617-234-9459 (Phone)

Mark Kritzman

Massachusetts Institute of Technology (MIT) - Sloan School of Management ( email )

100 Main Street
E62-416
Cambridge, MA 02142
United States

Register to save articles to
your library

Register

Paper statistics

Downloads
1,602
Abstract Views
4,908
rank
10,262
PlumX Metrics