IDEAS home Printed from https://ideas.repec.org/a/inm/oropre/v69y2021i4p1134-1157.html
   My bibliography  Save this article

A Large-Scale Optimization Model for Replicating Portfolios in the Life Insurance Industry

Author

Listed:
  • Maximilian Adelmann

    (Department of Business Administration, University of Zurich, 8032 Zurich, Switzerland)

  • Lucio Fernandez-Arjona

    (Department of Business Administration, University of Zurich, 8032 Zurich, Switzerland)

  • Janos Mayer

    (Department of Business Administration, University of Zurich, 8032 Zurich, Switzerland)

  • Karl Schmedders

    (IMD Lausanne, 1001 Lausanne, Switzerland)

Abstract

Replicating portfolios have emerged as an important tool in the life insurance industry, used for the valuation of companies’ liabilities. This paper describes the replicating portfolio (RP) model used to approximate life insurance liabilities in a large global insurance company. We describe the challenges presented by the latest solvency regimes in Europe and how the RP model enables this company to comply with the Swiss Solvency Test. The model minimizes the L 1 error between the discounted life insurance liability cash flows and the discounted RP cash flows over a multiperiod time horizon for a broad range of different future economic scenarios. A numerical application of the RP model to empirical data sets demonstrates that the model delivers RPs that match the liabilities and perform well for economic capital calculations.

Suggested Citation

  • Maximilian Adelmann & Lucio Fernandez-Arjona & Janos Mayer & Karl Schmedders, 2021. "A Large-Scale Optimization Model for Replicating Portfolios in the Life Insurance Industry," Operations Research, INFORMS, vol. 69(4), pages 1134-1157, July.
    • Handle: RePEc:inm:oropre:v:69:y:2021:i:4:p:1134-1157
    DOI: 10.1287/opre.2020.2098
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/opre.2020.2098
    Download Restriction: no
    File URL: https://libkey.io/10.1287/opre.2020.2098?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
    ---><---

    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:oropre:v:69:y:2021:i:4:p:1134-1157. 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.