Generalised Mean-Variance Analysis and Robust Portfolio Diversification

Stephen M. Wright and S. E. Satchell

Cambridge Working Papers in Economics from Faculty of Economics, University of Cambridge

Abstract: This paper presents a new approach to portfolio optimisation that we call generalised mean-variance (GMV) analysis. One important case of this approach is based on the stocks m-tile (or quantile): if m = n, where n is the number of stocks, m-tile membership becomes rank. Our analysis is the rank equivalent of conventional Markowitz Mean Variance analysis. The first stage to generate rank probability statistics using, historic data, Monte Carlo analysis or direct user input. The second stage is optimisation based on those rank statistics to calculate recommended portfolio weights. Our optimisation uses state preference theory to derive an objective function that can be minimised using standard quadratic programming techniques. We deal with some advantages of this method including a more intuitive fully diversified (or minimum risk) position on the efficient frontier with all the portfolio holdings equally weighted.

Keywords: Mean Variance Analysis; Diversification; Portfolio Construction; Forecasts. (search for similar items in EconPapers)
JEL-codes: C15 C52 C53 G00 (search for similar items in EconPapers)
Pages: 16
Date: 2002-01
Note: EM
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://files.econ.cam.ac.uk/repec/cam/pdf/wp0201.pdf (application/pdf)
Our link check indicates that this URL is bad, the error code is: 404 Not Found

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:cam:camdae:0201

Access Statistics for this paper

More papers in Cambridge Working Papers in Economics from Faculty of Economics, University of Cambridge
Bibliographic data for series maintained by Jake Dyer ().