IDEAS home Printed from
   My bibliography  Save this article

A Stochastic Programming Model to Minimize Volume Liquidity Risk in Commodity Trading



The goal of this paper is to study a very important risk metric in commodity trading: volume liquidity risk. It begins by examining the statistical properties of volume and settlement price change of futures contracts of different maturities. The results are used in the construction of a model for the minimization of volume liquidity risk – the inability to cover an unprofitable position due to lack of trading volume. The model is embedded in a stochastic program designed to construct a portfolio of futures contracts of different maturities with the aim of minimizing price and volume liquidity risk. The results of the case study (grain market) show that the model predicts the best spread trade accurately in 75 percent of cases. In the remaining cases the inaccuracy is due to the market shock present in the year 2008. A tool has been coded in Excel VBA to make the model available to traders and risk managers. This contribution directly relates to Energy ETF recent issues (i.e., roll-over).

Suggested Citation

  • Fragniere, Emmanuel & Markov, Iliya, 2011. "A Stochastic Programming Model to Minimize Volume Liquidity Risk in Commodity Trading," Journal of Financial Transformation, Capco Institute, vol. 32, pages 133-141.
  • Handle: RePEc:ris:jofitr:1460

    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. Arnaud Doucet & Vladislav Tadić, 2003. "Parameter estimation in general state-space models using particle methods," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 55(2), pages 409-422, June.
    2. Roncalli, Thierry & Teiletche, Jérôme, 2008. "An Alternative Approach to Alternative Beta," Journal of Financial Transformation, Capco Institute, vol. 24, pages 43-52.
    3. repec:dau:papers:123456789/812 is not listed on IDEAS
    4. Vikas Agarwal, 2004. "Risks and Portfolio Decisions Involving Hedge Funds," Review of Financial Studies, Society for Financial Studies, vol. 17(1), pages 63-98.
    5. Antonio Diez De Los Rios & René Garcia, 2011. "Assessing and valuing the nonlinear structure of hedge fund returns," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(2), pages 193-212, March.
    6. Amin, Gaurav S. & Kat, Harry M., 2003. "Hedge Fund Performance 1990–2000: Do the “Money Machines” Really Add Value?," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 38(02), pages 251-274, June.
    7. Merton, Robert C, 1981. "On Market Timing and Investment Performance. I. An Equilibrium Theory of Value for Market Forecasts," The Journal of Business, University of Chicago Press, vol. 54(3), pages 363-406, July.
    8. Fung, W. & Hsieh, D A., 2007. "Hedge fund replication strategies: implications for investors and regulators," Financial Stability Review, Banque de France, issue 10, pages 55-66, April.
    9. Fung, William & Hsieh, David A, 1997. "Empirical Characteristics of Dynamic Trading Strategies: The Case of Hedge Funds," Review of Financial Studies, Society for Financial Studies, vol. 10(2), pages 275-302.
    10. Fung, William & Hsieh, David A., 1999. "A primer on hedge funds," Journal of Empirical Finance, Elsevier, vol. 6(3), pages 309-331, September.
    Full references (including those not matched with items on IDEAS)

    More about this item


    Stochastic Programming; Commodity Trading; ETF; Liquidity Risk; Futures; Forward Curve;

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing


    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:ris:jofitr:1460. 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: (Prof. Shahin Shojai). 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.