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Decomposing Dynamic Risks into Risk Components

Author

Listed:
  • Katja Schilling

    (Institut für Versicherungswissenschaften, Universität Ulm, 89081 Ulm, Germany)

  • Daniel Bauer

    (Department of Risk and Insurance, University of Wisconsin–Madison, Madison, Wisconsin 53706)

  • Marcus C. Christiansen

    (Department of Risk and Insurance, University of Wisconsin–Madison, Madison, Wisconsin 53706)

  • Alexander Kling

    (Institut für Finanz- und Aktuarwissenschaften, 89081 Ulm, Germany)

Abstract

The decomposition of dynamic risks a company faces into components associated with various sources of risk, such as financial risks, aggregate economic risks, or industry-specific risk drivers, is of significant relevance in view of risk management and product design, particularly in (life) insurance. Nevertheless, although several decomposition approaches have been proposed, no systematic analysis is available. This paper closes this gap in literature by introducing properties for meaningful risk decompositions and demonstrating that proposed approaches violate at least one of these properties. As an alternative, we propose a novel martingale representation theorem ( MRT ) decomposition that relies on martingale representation and show that it satisfies all of the properties. We discuss its calculation and present detailed examples illustrating its applicability.

Suggested Citation

  • Katja Schilling & Daniel Bauer & Marcus C. Christiansen & Alexander Kling, 2020. "Decomposing Dynamic Risks into Risk Components," Management Science, INFORMS, vol. 66(12), pages 5738-5756, December.
  • Handle: RePEc:inm:ormnsc:v:66:y:12:i:2020:p:5738-5756
    DOI: 10.1287/mnsc.2019.3522
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    References listed on IDEAS

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    Cited by:

    1. Gero Junike & Hauke Stier & Marcus C. Christiansen, 2022. "Sequential decompositions at their limit," Papers 2212.06733, arXiv.org, revised Apr 2023.
    2. Lazar, Emese & Qi, Shuyuan, 2022. "Model risk in the over-the-counter market," European Journal of Operational Research, Elsevier, vol. 298(2), pages 769-784.
    3. Marcus C. Christiansen, 2021. "Time-dynamic evaluations under non-monotone information generated by marked point processes," Finance and Stochastics, Springer, vol. 25(3), pages 563-596, July.
    4. Aigner, Philipp & Schlütter, Sebastian, 2023. "Enhancing gradient capital allocation with orthogonal convexity scenarios," ICIR Working Paper Series 47/23, Goethe University Frankfurt, International Center for Insurance Regulation (ICIR).

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