IDEAS home Printed from
   My bibliography  Save this article

Fast Methods For Large-Scale Non-Elliptical Portfolio Optimization



    () (Swiss Banking Institute, University of Zurich, Switzerland;
    Swiss Finance Institute, Switzerland)


Simple, fast methods for modeling the portfolio distribution corresponding to a non-elliptical, leptokurtic, asymmetric, and conditionally heteroskedastic set of asset returns are entertained. Portfolio optimization via simulation is demonstrated, and its benefits are discussed. An augmented mixture of normals model is shown to be superior to both standard (no short selling) Markowitz and the equally weighted portfolio in terms of out of sample returns and Sharpe ratio performance.

Suggested Citation

  • Marc S. Paolella, 2014. "Fast Methods For Large-Scale Non-Elliptical Portfolio Optimization," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 9(02), pages 1-32.
  • Handle: RePEc:wsi:afexxx:v:09:y:2014:i:02:n:s2010495214400016
    DOI: 10.1142/S2010495214400016

    Download full text from publisher

    File URL:
    Download Restriction: Access to full text is restricted to subscribers.

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    1. AfDB AfDB, . "African Statistical Journal Vol.16," African Statistical Journal, African Development Bank, number 455.
    2. Unknown, 2013. "JAAE Manuscript Submission Guidelines," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 0, pages 1-2, August.
    3. Ghassan Al-Utaibi, Ph.D, 2013. "Predicting Future Health Demands in Jordan," International Journal of Business and Social Research, MIR Center for Socio-Economic Research, vol. 3(7), pages 130-136, July.
    4. Gilli, Manfred & Maringer, Dietmar & Schumann, Enrico, 2011. "Numerical Methods and Optimization in Finance," Elsevier Monographs, Elsevier, edition 1, number 9780123756626, August.
    5. Rachel E. S. Ziemba & William T. Ziemba, 2013. "The January Barometer," World Scientific Book Chapters,in: Investing in the Modern Age, chapter 16, pages 191-203 World Scientific Publishing Co. Pte. Ltd..
    6. Unknown, 2013. "JARE Editors’ Report," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 0(Number 3), pages 1-5.
    7. Bengtsson, Christoffer, 2003. "The Impact of Estimation Error on Portfolio Selection for Investors with Constant Relative Risk Aversion," Working Papers 2003:17, Lund University, Department of Economics, revised 29 Apr 2004.
    8. Ocde, 2013. "Documents et textes juridiques," Bulletin de droit nucléaire, Éditions OCDE, vol. 2012(2), pages 179-274.
    9. Unknown, 2013. "JAAE backmatter," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 0(Number 2), pages 1-2, May.
    10. William T. Shaw, 2010. "Monte Carlo Portfolio Optimization for General Investor Risk-Return Objectives and Arbitrary Return Distributions: a Solution for Long-only Portfolios," Papers 1008.3718,
    11. ., 2013. "A trajectory of legal tricks (hiyal)," Chapters,in: What is Wrong with Islamic Economics?, chapter 20, pages 337-401 Edward Elgar Publishing.
    12. van Damme, E.E.C., 2013. "Deugdelijke economen," Other publications TiSEM 15ce252a-21e4-411e-9702-4, Tilburg University, School of Economics and Management.
    Full references (including those not matched with items on IDEAS)


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

    Cited by:

    1. repec:gam:jecnmx:v:5:y:2017:i:2:p:18-:d:97715 is not listed on IDEAS

    More about this item


    Expected shortfall; GARCH; mixture distributions; portfolio allocation; shrinkage estimation; simulation; weighted likelihood; C51; C53; G11; G17;

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation


    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:wsi:afexxx:v:09:y:2014:i:02:n:s2010495214400016. 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: (Tai Tone Lim). 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.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with 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.