IDEAS home Printed from https://ideas.repec.org/a/gam/jjrfmx/v10y2017i2p11-d98991.html
   My bibliography  Save this article

The Solvency II Standard Formula, Linear Geometry, and Diversification

Author

Listed:
  • Joachim Paulusch

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

Abstract

The core of risk aggregation in the Solvency II Standard Formula is the so-called square root formula. We argue that it should be seen as a means for the aggregation of different risks to an overall risk rather than being associated with variance-covariance based risk analysis. Considering the Solvency II Standard Formula from the viewpoint of linear geometry, we immediately find that it defines a norm and therefore provides a homogeneous and sub-additive tool for risk aggregation. Hence, Euler’s Principle for the reallocation of risk capital applies and yields explicit formulas for capital allocation in the framework given by the Solvency II Standard Formula. This gives rise to the definition of diversification functions , which we define as monotone, subadditive, and homogeneous functions on a convex cone. Diversification functions constitute a class of models for the study of the aggregation of risk and diversification. The aggregation of risk measures using a diversification function preserves the respective properties of these risk measures. Examples of diversification functions are given by seminorms, which are monotone on the convex cone of non-negative vectors. Each L p norm has this property, and any scalar product given by a non-negative positive semidefinite matrix does as well. In particular, the Standard Formula is a diversification function and hence a risk measure that preserves homogeneity, subadditivity and convexity.

Suggested Citation

  • Joachim Paulusch, 2017. "The Solvency II Standard Formula, Linear Geometry, and Diversification," JRFM, MDPI, vol. 10(2), pages 1-12, May.
  • Handle: RePEc:gam:jjrfmx:v:10:y:2017:i:2:p:11-:d:98991
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1911-8074/10/2/11/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1911-8074/10/2/11/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mittnik, Stefan, 2014. "VaR-implied tail-correlation matrices," Economics Letters, Elsevier, vol. 122(1), pages 69-73.
    2. Rachel Campbell & Kees Koedijk & Paul Kofman, 2002. "Increased Correlation in Bear Markets," Financial Analysts Journal, Taylor & Francis Journals, vol. 58(1), pages 87-94, January.
    3. Filipović, Damir, 2009. "Multi-Level Risk Aggregation," ASTIN Bulletin, Cambridge University Press, vol. 39(2), pages 565-575, November.
    4. Michael Kalkbrener, 2005. "An Axiomatic Approach To Capital Allocation," Mathematical Finance, Wiley Blackwell, vol. 15(3), pages 425-437, July.
    5. Boonen, Tim J. & Tsanakas, Andreas & Wüthrich, Mario V., 2017. "Capital allocation for portfolios with non-linear risk aggregation," Insurance: Mathematics and Economics, Elsevier, vol. 72(C), pages 95-106.
    6. Jan Dhaene & Andreas Tsanakas & Emiliano A. Valdez & Steven Vanduffel, 2012. "Optimal Capital Allocation Principles," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 79(1), pages 1-28, March.
    7. Kofman, Paul & Koedijk, Kees & Campbell, Rachel, 2002. "Increased Correlation in Bear markets: A Downside Risk Perspective," CEPR Discussion Papers 3172, C.E.P.R. Discussion Papers.
    8. Dirk Tasche, 2007. "Capital Allocation to Business Units and Sub-Portfolios: the Euler Principle," Papers 0708.2542, arXiv.org, revised Jun 2008.
    9. Ivan Granito & Paolo De Angelis, 2015. "Capital allocation and risk appetite under Solvency II framework," Papers 1511.02934, arXiv.org.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Jacopo Giacomelli & Luca Passalacqua, 2021. "Unsustainability Risk of Bid Bonds in Public Tenders," Mathematics, MDPI, vol. 9(19), pages 1-21, September.
    2. Paulusch, Joachim & Schlütter, Sebastian, 2021. "Sensitivity-implied tail-correlation matrices," ICIR Working Paper Series 33/19, Goethe University Frankfurt, International Center for Insurance Regulation (ICIR), revised 2021.
    3. Aigner, Philipp, 2023. "Identifying scenarios for the own risk and solvency assessment of insurance companies," ICIR Working Paper Series 48/23, Goethe University Frankfurt, International Center for Insurance Regulation (ICIR).
    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).

    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. Paulusch, Joachim & Schlütter, Sebastian, 2022. "Sensitivity-implied tail-correlation matrices," Journal of Banking & Finance, Elsevier, vol. 134(C).
    2. Wang, Wei & Xu, Huifu & Ma, Tiejun, 2023. "Optimal scenario-dependent multivariate shortfall risk measure and its application in risk capital allocation," European Journal of Operational Research, Elsevier, vol. 306(1), pages 322-347.
    3. Paulusch, Joachim & Schlütter, Sebastian, 2021. "Sensitivity-implied tail-correlation matrices," ICIR Working Paper Series 33/19, Goethe University Frankfurt, International Center for Insurance Regulation (ICIR), revised 2021.
    4. Jilber Urbina & Miguel Santolino & Montserrat Guillen, 2021. "Covariance Principle for Capital Allocation: A Time-Varying Approach," Mathematics, MDPI, vol. 9(16), pages 1-13, August.
    5. Kang, Woo-Young & Poshakwale, Sunil, 2019. "A new approach to optimal capital allocation for RORAC maximization in banks," Journal of Banking & Finance, Elsevier, vol. 106(C), pages 153-165.
    6. Stephen J. Mildenhall, 2017. "Actuarial Geometry," Risks, MDPI, vol. 5(2), pages 1-44, June.
    7. Jaume Belles-Sampera & Montserrat Guillen & Miguel Santolino, 2023. "Haircut Capital Allocation as the Solution of a Quadratic Optimisation Problem," Mathematics, MDPI, vol. 11(18), pages 1-17, September.
    8. Takaaki Koike & Marius Hofert, 2019. "Markov Chain Monte Carlo Methods for Estimating Systemic Risk Allocations," Papers 1909.11794, arXiv.org, revised May 2020.
    9. Dóra Balog, 2017. "Capital Allocation in the Insurance Sector," Financial and Economic Review, Magyar Nemzeti Bank (Central Bank of Hungary), vol. 16(3), pages 74-97.
    10. Boonen, Tim J. & Guillen, Montserrat & Santolino, Miguel, 2019. "Forecasting compositional risk allocations," Insurance: Mathematics and Economics, Elsevier, vol. 84(C), pages 79-86.
    11. Bauer, Daniel & Kamiya, Shinichi & Ping, Xiaohu & Zanjani, George, 2019. "Dynamic capital allocation with irreversible investments," Insurance: Mathematics and Economics, Elsevier, vol. 85(C), pages 138-152.
    12. Dóra Balog, 2010. "Risk based capital allocation," Proceedings of FIKUSZ '10, in: László Áron Kóczy (ed.),Proceedings of FIKUSZ 2010, pages 17-26, Óbuda University, Keleti Faculty of Business and Management.
    13. Pesenti, Silvana M. & Tsanakas, Andreas & Millossovich, Pietro, 2018. "Euler allocations in the presence of non-linear reinsurance: Comment on Major (2018)," Insurance: Mathematics and Economics, Elsevier, vol. 83(C), pages 29-31.
    14. Takaaki Koike & Cathy W. S. Chen & Edward M. H. Lin, 2024. "Forecasting and Backtesting Gradient Allocations of Expected Shortfall," Papers 2401.11701, arXiv.org, revised Jun 2024.
    15. Furman, Edward & Kye, Yisub & Su, Jianxi, 2021. "Multiplicative background risk models: Setting a course for the idiosyncratic risk factors distributed phase-type," Insurance: Mathematics and Economics, Elsevier, vol. 96(C), pages 153-167.
    16. Grechuk, Bogdan, 2023. "Extended gradient of convex function and capital allocation," European Journal of Operational Research, Elsevier, vol. 305(1), pages 429-437.
    17. Takaaki Koike & Mihoko Minami, 2017. "Estimation of Risk Contributions with MCMC," Papers 1702.03098, arXiv.org, revised Jan 2019.
    18. 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).
    19. Csóka, Péter & Bátyi, Tamás László & Pintér, Miklós & Balog, Dóra, 2011. "Tőkeallokációs módszerek és tulajdonságaik a gyakorlatban [Methods of capital allocation and their characteristics in practice]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(7), pages 619-632.
    20. Alcock, Jamie & Sinagl, Petra, 2022. "International determinants of asymmetric dependence in investment returns," Journal of International Money and Finance, Elsevier, vol. 122(C).

    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:jjrfmx:v:10:y:2017:i:2:p:11-:d:98991. 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.