IDEAS home Printed from
   My bibliography  Save this paper

Worst-Case Design In Optimal Portfolios


  • Berc Rustem

    (Imperial College of Science, Technology & Medicine)


Optimal decisions robust to future uncertainties are considered. Both continuous and discrete sets of scenarios are discussed with algorithms for solving both cases. In the case of the former a quasi-Newton algorithm is discussed and in the case of the latter, a fast and easily implementable approach is introduced. Optimal portfolio results are used to illustrate the robustness properties of the strategy. A macroeconomic example is also considered.

Suggested Citation

  • Berc Rustem, 2000. "Worst-Case Design In Optimal Portfolios," Computing in Economics and Finance 2000 14, Society for Computational Economics.
  • Handle: RePEc:sce:scecf0:14

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    References listed on IDEAS

    1. John C. Harsanyi & Reinhard Selten, 1988. "A General Theory of Equilibrium Selection in Games," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262582384, January.
    2. Blume, Lawrence E. & Easley, David, 1982. "Learning to be rational," Journal of Economic Theory, Elsevier, vol. 26(2), pages 340-351, April.
    3. Cooper,Russell, 1999. "Coordination Games," Cambridge Books, Cambridge University Press, number 9780521570176, March.
    4. Arifovic, Jasmina, 1994. "Genetic algorithm learning and the cobweb model," Journal of Economic Dynamics and Control, Elsevier, vol. 18(1), pages 3-28, January.
    5. Arifovic, Jasmina & Eaton, Curtis, 1995. "Coordination via Genetic Learning," Computational Economics, Springer;Society for Computational Economics, vol. 8(3), pages 181-203, August.
    6. Bullard, James & Duffy, John, 1999. "Using Genetic Algorithms to Model the Evolution of Heterogeneous Beliefs," Computational Economics, Springer;Society for Computational Economics, vol. 13(1), pages 41-60, February.
    7. Arifovic, Jasmina, 1995. "Genetic algorithms and inflationary economies," Journal of Monetary Economics, Elsevier, vol. 36(1), pages 219-243, August.
    8. Sacco, Pier Luigi, 1994. "Can People Learn Rational Expectations? An 'Ecological' Approach," Journal of Evolutionary Economics, Springer, vol. 4(1), pages 35-43, March.
    9. Sent,Esther-Mirjam, 2006. "The Evolving Rationality of Rational Expectations," Cambridge Books, Cambridge University Press, number 9780521027717, March.
    10. Arifovic, Jasmina, 1996. "The Behavior of the Exchange Rate in the Genetic Algorithm and Experimental Economies," Journal of Political Economy, University of Chicago Press, vol. 104(3), pages 510-541, June.
    11. Marimon, Ramon & McGrattan, Ellen & Sargent, Thomas J., 1990. "Money as a medium of exchange in an economy with artificially intelligent agents," Journal of Economic Dynamics and Control, Elsevier, vol. 14(2), pages 329-373, May.
    12. Riechmann, Thomas, 1998. "Genetic Algorithms and Economic Evolution," Hannover Economic Papers (HEP) dp-219, Leibniz Universit├Ąt Hannover, Wirtschaftswissenschaftliche Fakult├Ąt.
    13. Cooper,Russell, 1999. "Coordination Games," Cambridge Books, Cambridge University Press, number 9780521578967, March.
    14. Selten, Reinhard, 1991. "Evolution, learning, and economic behavior," Games and Economic Behavior, Elsevier, vol. 3(1), pages 3-24, February.
    15. Herbert Dawid, 1996. "Learning of cycles and sunspot equilibria by Genetic Algorithms (*)," Journal of Evolutionary Economics, Springer, vol. 6(4), pages 361-373.
    16. Vriend, Nicolaas J., 2000. "An illustration of the essential difference between individual and social learning, and its consequences for computational analyses," Journal of Economic Dynamics and Control, Elsevier, vol. 24(1), pages 1-19, January.
    Full references (including those not matched with items on IDEAS)

    More about this item


    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:sce:scecf0:14. 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: (Christopher F. Baum). 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.