New light on the portfolio allocation problem

R. A. Maller and D. A. Turkington

Mathematical Methods of Operations Research, 2003, vol. 56, issue 3, 511 pages

Abstract: The basics of the mean-variance portfolio optimisation procedure have been well understood since the seminal work of Markowitz in the 1950's. A vector x of asset weights, restricted only by requiring its components to add to 1, is to be chosen so that the linear combination μ p =x ′ μ of the expected asset returns μ (or, expected excess returns), which represents the expected return on a portfolio, is maximised for a specified level of “risk”, σ p , the standard deviation of the portfolio. The efficient frontier is the curve traced out in (μ p , σ p ) space by portfolios whose return/risk tradeoff is optimal in this sense. The portfolio with the maximum Sharpe ratio is the portfolio with the highest return/risk tradeoff achievable from the assets, and under some conditions can be obtained as the point of tangency of a line from the origin to the efficient frontier, as is well known. But when the tangency approach fails, which it commonly can, the question arises as to the maximum Sharpe ratio achievable from the assets. This problem, which has not been dealt with before, is solved explicitly in this paper, and the corresponding optimal portfolio found. The suggested procedure is easily implemented when the usual inputs – estimates of mean excess returns and their covariance matrix – are available. Copyright Springer-Verlag Berlin Heidelberg 2003

Keywords: Key Words: Optimal Portfolio Allocation; Markowitz Mean-Variance Optimisation; Efficient Frontier; Sharpe Ratio (search for similar items in EconPapers)
Date: 2003
References: Add references at CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://hdl.handle.net/10.1007/s001860200211 (text/html)
Access to full text is restricted to subscribers.

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:spr:mathme:v:56:y:2003:i:3:p:501-511

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/00186

DOI: 10.1007/s001860200211

Access Statistics for this article

Mathematical Methods of Operations Research is currently edited by Oliver Stein

More articles in Mathematical Methods of Operations Research from Springer, Gesellschaft für Operations Research (GOR), Nederlands Genootschap voor Besliskunde (NGB)
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().