IDEAS home Printed from https://ideas.repec.org/a/gam/jrisks/v12y2024i2p23-d1328788.html
   My bibliography  Save this article

Quadratic Unconstrained Binary Optimization Approach for Incorporating Solvency Capital into Portfolio Optimization

Author

Listed:
  • Ivica Turkalj

    (Department of Financial Mathematics, Fraunhofer ITWM, Fraunhofer-Platz 1, 67663 Kaiserslautern, Germany)

  • Mohammad Assadsolimani

    (DZ BANK AG, Platz der Republik, 60325 Frankfurt am Main, Germany)

  • Markus Braun

    (JoS QUANTUM GmbH, Platz der Einheit 2, 60327 Frankfurt Am Main, Germany)

  • Pascal Halffmann

    (Department of Financial Mathematics, Fraunhofer ITWM, Fraunhofer-Platz 1, 67663 Kaiserslautern, Germany)

  • Niklas Hegemann

    (JoS QUANTUM GmbH, Platz der Einheit 2, 60327 Frankfurt Am Main, Germany)

  • Sven Kerstan

    (JoS QUANTUM GmbH, Platz der Einheit 2, 60327 Frankfurt Am Main, Germany)

  • Janik Maciejewski

    (R+V Lebensversicherung AG, Raiffeisenplatz 2, 65189 Wiesbaden, Germany)

  • Shivam Sharma

    (Department of Financial Mathematics, Fraunhofer ITWM, Fraunhofer-Platz 1, 67663 Kaiserslautern, Germany)

  • Yuanheng Zhou

    (JoS QUANTUM GmbH, Platz der Einheit 2, 60327 Frankfurt Am Main, Germany)

Abstract

In this paper, we consider the inclusion of the solvency capital requirement (SCR) into portfolio optimization by the use of a quadratic proxy model. The Solvency II directive requires insurance companies to calculate their SCR based on the complete loss distribution for the upcoming year. Since this task is, in general, computationally challenging for insurance companies (and therefore, not taken into account during portfolio optimization), employing more feasible proxy models provides a potential solution to this computational difficulty. Here, we present an approach that is also suitable for future applications in quantum computing. We analyze the approximability of the solvency capital ratio in a quadratic form using machine learning techniques. This allows for an easier consideration of the SCR in the classical mean-variance analysis. In addition, it allows the problem to be formulated as a quadratic unconstrained binary optimization (QUBO), which benefits from the potential speedup of quantum computing. We provide a detailed description of our model and the translation into a QUBO. Furthermore, we investigate the performance of our approach through experimental studies.

Suggested Citation

  • Ivica Turkalj & Mohammad Assadsolimani & Markus Braun & Pascal Halffmann & Niklas Hegemann & Sven Kerstan & Janik Maciejewski & Shivam Sharma & Yuanheng Zhou, 2024. "Quadratic Unconstrained Binary Optimization Approach for Incorporating Solvency Capital into Portfolio Optimization," Risks, MDPI, vol. 12(2), pages 1-17, January.
    • Handle: RePEc:gam:jrisks:v:12:y:2024:i:2:p:23-:d:1328788
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-9091/12/2/23/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-9091/12/2/23/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kerstin Dächert & Ria Grindel & Elisabeth Leoff & Jonas Mahnkopp & Florian Schirra & Jörg Wenzel, 2022. "Multicriteria asset allocation in practice," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 44(2), pages 349-373, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Salo, Ahti & Doumpos, Michalis & Liesiö, Juuso & Zopounidis, Constantin, 2024. "Fifty years of portfolio optimization," European Journal of Operational Research, Elsevier, vol. 318(1), pages 1-18.

    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:gam:jrisks:v:12:y:2024:i:2:p:23-:d:1328788. 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.

    If CitEc recognized a bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.