IDEAS home Printed from
   My bibliography  Save this article

Optimizing Benchmark-Based Utility Functions


  • David Morton
  • Elmira Popova
  • Ivilina Popova
  • Ming Zhong


We Consider Four Utility Functions, Each Of Which Incorporates A Benchmark To Better Capture The Motivations Of Today's Portfolio Managers. Assuming Instrument Returns Are Normally Distributed, We Establish Conditions Under Which Optimal Portfolios For These Utilities Are Mean-Variance Efficient And We Briefly Discuss Computing Solutions Of The Models Via Standard Nonlinear Programming Tools. When Returns Are Not Normally Distributed, We Cannot, In General, Solve The Optimal Allocation Problems Exactly. Instead We Use An Approximation Procedure Rooted In Monte Carlo Simulation. Our Approach Requires Mixed-Integer Programming, And We Describe Computational Enhancements That Significantly Improve Our Ability To Solve These Models.

Suggested Citation

  • David Morton & Elmira Popova & Ivilina Popova & Ming Zhong, 2003. "Optimizing Benchmark-Based Utility Functions," Bulletin of the Czech Econometric Society, The Czech Econometric Society, vol. 10(18).
  • Handle: RePEc:czx:journl:v:10:y:2003:i:18:id:117

    Download full text from publisher

    File URL:
    Download Restriction: no


    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.

    Cited by:

    1. Dmitriy Volinskiy & Michele Veeman & Wiktor Adamowicz, 2011. "Allocation of public funds to R&D: a portfolio choice-styled decision model and a biotechnology case study," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 34(2), pages 121-139, November.
    2. Morton, David P. & Popova, Elmira & Popova, Ivilina, 2006. "Efficient fund of hedge funds construction under downside risk measures," Journal of Banking & Finance, Elsevier, vol. 30(2), pages 503-518, February.

    More about this item


    Portfolio Allocation; Mean-Variance Efficiency; Stochastic Programming; Monte Carlo Simulation;

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory


    Access and download statistics


    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:czx:journl:v:10:y:2003:i:18:id:117. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Jozef Barunik). General contact details of provider: .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.