IDEAS home Printed from
   My bibliography  Save this paper

Conditioned Higher Moment Portfolio Optimisation Using Optimal Control


  • Marc Boissaux


  • Jang Schiltz



Within a traditional context of myopic discrete-time mean-variance portfolio investments, the problem of conditioned optimisation, in which predictive information about returns contained in a signal is used to inform the choice of portfolio weights, was first expressed and solved in concrete terms by [1]. An optimal control formulation of conditioned portfolio problems was proposed and justified by [2]. This opens up the possibility of solving variants of the basic problem that do not allow for closed-form solutions through the use of standard numerical algorithms used for the discretisation of optimal control problems. The present paper applies this formulation to set and solve variants of the conditioned portfolio problem which use the third and fourth moments as well as the variance. Using backtests over a realistic data set, the performance of strategies resulting from conditioned optimisation is then compared to that obtained using analogous optimisation strategies which do not exploit conditioning information. In particular, we report on both ex ante improvements to the accessible expected return-risk boundaries and the ex post results obtained.

Suggested Citation

  • Marc Boissaux & Jang Schiltz, 2012. "Conditioned Higher Moment Portfolio Optimisation Using Optimal Control," LSF Research Working Paper Series 12-2, Luxembourg School of Finance, University of Luxembourg.
  • Handle: RePEc:crf:wpaper:12-2

    Download full text from publisher

    File URL:
    Download Restriction: no

    More about this item


    Skewness; Kurtosis; Optimal Control; Portfolio Optimization;

    JEL classification:

    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

    NEP fields

    This paper has been announced in the following NEP Reports:


    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:crf:wpaper:12-2. 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: (Martine Zenner). 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.