IDEAS home Printed from https://ideas.repec.org/a/inm/orinte/v35y2005i5p370-380.html
   My bibliography  Save this article

GE Asset Management, Genworth Financial, and GE Insurance Use a Sequential-Linear-Programming Algorithm to Optimize Portfolios

Author

Listed:
  • Kete Charles Chalermkraivuth

    (General Electric Global Research Center, 1 Research Circle, Schenectady, New York 12309)

  • Srinivas Bollapragada

    (General Electric Global Research Center, 1 Research Circle, Schenectady, New York 12309)

  • Michael C. Clark

    (General Electric Global Research Center, 1 Research Circle, Schenectady, New York 12309)

  • John Deaton

    (General Electric Global Research Center, 1 Research Circle, Schenectady, New York 12309)

  • Lynn Kiaer

    (General Electric Global Research Center, 1 Research Circle, Schenectady, New York 12309)

  • John P. Murdzek

    (General Electric Global Research Center, 1 Research Circle, Schenectady, New York 12309)

  • Walter Neeves

    (General Electric Global Research Center, 1 Research Circle, Schenectady, New York 12309)

  • Bernhard J. Scholz

    (General Electric Global Research Center, 1 Research Circle, Schenectady, New York 12309)

  • David Toledano

    (General Electric Global Research Center, 1 Research Circle, Schenectady, New York 12309)

Abstract

GE Asset Management Incorporated (GEAM), a wholly owned subsidiary of General Electric Company (GE), manages investment portfolios on behalf of various GE units and over 200 unaffiliated clients worldwide, including Genworth Financial (Genworth) and GE Insurance (GEI) portfolios worth billions of dollars. GEAM invests portfolios of assets—derived from cash flows for various insurance, reinsurance, and financial products—primarily in corporate and government bonds in accordance with risk and regulatory constraints. In asset-liability management (ALM) applications, portfolio managers try to maximize return or minimize risk and match the characteristics of asset portfolios with corresponding liabilities. While risk is widely represented by variance or volatility, it is usually a nonlinear measure; ALM portfolio managers traditionally need to use linear risk sensitivities for computational tractability. We developed a novel, sequential-linear-programming algorithm that handles the nonlinearity iteratively but efficiently. Patented and implemented on a limited basis since 2003, GE used it to optimize more than 30 portfolios valued at over $30 billion. It is now in broader use at GEAM, GEI, and Genworth. Hypothetically, based on $100 billion of assets, the present value of potential benefits could approximate $75 million over five years.

Suggested Citation

  • Kete Charles Chalermkraivuth & Srinivas Bollapragada & Michael C. Clark & John Deaton & Lynn Kiaer & John P. Murdzek & Walter Neeves & Bernhard J. Scholz & David Toledano, 2005. "GE Asset Management, Genworth Financial, and GE Insurance Use a Sequential-Linear-Programming Algorithm to Optimize Portfolios," Interfaces, INFORMS, vol. 35(5), pages 370-380, October.
  • Handle: RePEc:inm:orinte:v:35:y:2005:i:5:p:370-380
    DOI: 10.1287/inte.1050.0164
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/inte.1050.0164
    Download Restriction: no

    File URL: https://libkey.io/10.1287/inte.1050.0164?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

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


    Cited by:

    1. Fabio Vitor & Todd Easton, 2018. "The double pivot simplex method," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 87(1), pages 109-137, February.

    Corrections

    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:inm:orinte:v:35:y:2005:i:5:p:370-380. See general information about how to correct material in RePEc.

    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 bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.