IDEAS home Printed from https://ideas.repec.org/a/ksa/szemle/1182.html
   My bibliography  Save this article

Biztosítók kockázatdiverzifikációja
[Risk diversification of insurers]

Author

Listed:
  • Szüle, Borbála

Abstract

A biztosítók működését általában több homogén részállományból összetevődő heterogén biztosítási állomány jellemzi. A részállományok alkotta biztosítási portfólió esetében a kockázatdiverzifikáció vizsgálható a teljes állományra, illetve a részállományokra összesített kockázatok különbségeként, és elemezhető a kockázat és hozam kapcsolata alapján is. A biztosítók működésének főbb sajátosságait tartalmazó modellben azt mutatjuk meg, hogy a biztosítási portfólió esetében tapasztalható kockázatdiverzifikációs hatások milyen mértékben hasonlítanak a klasszikusnak számító, befektetésekkel foglalkozó Markowitz-féle portfólióelmélet által leírtakra. Modellünk alapján megállapítható: számos hasonlóságon túl a biztosító működési sajátosságai következtében a hatékony biztosítási portfóliók, illetve az optimális befektetési arányok meghatározása egyedi tulajdonságokkal jellemezhető. Journal of Economic Literature (JEL) kód: G11, G22.

Suggested Citation

  • Szüle, Borbála, 2010. "Biztosítók kockázatdiverzifikációja [Risk diversification of insurers]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(7), pages 634-651.
  • Handle: RePEc:ksa:szemle:1182
    as

    Download full text from publisher

    File URL: http://www.kszemle.hu/tartalom/letoltes.php?id=1182
    Download Restriction: Registration and subscription. 3-month embargo period to non-subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Mesfioui, Mhamed & Quessy, Jean-Francois, 2005. "Bounds on the value-at-risk for the sum of possibly dependent risks," Insurance: Mathematics and Economics, Elsevier, vol. 37(1), pages 135-151, August.
    2. Casper G. de Vries & Gennady Samorodnitsky & Bjørn N. Jorgensen & Sarma Mandira & Jon Danielsson, 2005. "Subadditivity Re–Examined: the Case for Value-at-Risk," FMG Discussion Papers dp549, Financial Markets Group.
    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. Alexander, Gordon J. & Baptista, Alexandre M. & Yan, Shu, 2013. "A comparison of the original and revised Basel market risk frameworks for regulating bank capital," Journal of Economic Behavior & Organization, Elsevier, vol. 85(C), pages 249-268.
    2. Alexander, Gordon J. & Baptista, Alexandre M. & Yan, Shu, 2012. "When more is less: Using multiple constraints to reduce tail risk," Journal of Banking & Finance, Elsevier, vol. 36(10), pages 2693-2716.
    3. Carole Bernard & Ludger Rüschendorf & Steven Vanduffel & Ruodu Wang, 2017. "Risk bounds for factor models," Finance and Stochastics, Springer, vol. 21(3), pages 631-659, July.
    4. Lazar, Emese & Zhang, Ning, 2019. "Model risk of expected shortfall," Journal of Banking & Finance, Elsevier, vol. 105(C), pages 74-93.
    5. Gaglianone, Wagner Piazza & Lima, Luiz Renato & Linton, Oliver & Smith, Daniel R., 2011. "Evaluating Value-at-Risk Models via Quantile Regression," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(1), pages 150-160.
    6. Hofert, Marius & McNeil, Alexander J., 2015. "Subadditivity of Value-at-Risk for Bernoulli random variables," Statistics & Probability Letters, Elsevier, vol. 98(C), pages 79-88.
    7. repec:hal:journl:hal-00880258 is not listed on IDEAS
    8. Jaume Belles‐Sampera & Montserrat Guillén & Miguel Santolino, 2014. "Beyond Value‐at‐Risk: GlueVaR Distortion Risk Measures," Risk Analysis, John Wiley & Sons, vol. 34(1), pages 121-134, January.
    9. Genest, Christian & Gerber, Hans U. & Goovaerts, Marc J. & Laeven, Roger J.A., 2009. "Editorial to the special issue on modeling and measurement of multivariate risk in insurance and finance," Insurance: Mathematics and Economics, Elsevier, vol. 44(2), pages 143-145, April.
    10. Oliver Kley & Claudia Kluppelberg & Gesine Reinert, 2014. "Risk in a large claims insurance market with bipartite graph structure," Papers 1410.8671, arXiv.org, revised Nov 2015.
    11. Kratz , Marie, 2013. "There is a VaR Beyond Usual Approximations," ESSEC Working Papers WP1317, ESSEC Research Center, ESSEC Business School.
    12. Bakshi, Gurdip & Panayotov, George, 2010. "First-passage probability, jump models, and intra-horizon risk," Journal of Financial Economics, Elsevier, vol. 95(1), pages 20-40, January.
    13. Dominique Guegan & Bertrand K Hassani, 2014. "Distortion Risk Measures or the Transformation of Unimodal Distributions into Multimodal Functions," Documents de travail du Centre d'Economie de la Sorbonne 14008, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    14. Marie Kratz, 2013. "There is a VaR Beyond Usual Approximations," Working Papers hal-00880258, HAL.
    15. Embrechts, Paul & Neslehová, Johanna & Wüthrich, Mario V., 2009. "Additivity properties for Value-at-Risk under Archimedean dependence and heavy-tailedness," Insurance: Mathematics and Economics, Elsevier, vol. 44(2), pages 164-169, April.
    16. Joaquin, Domingo Castelo, 2009. "Value at risk: Is a theoretically consistent axiomatic formulation possible?," The Quarterly Review of Economics and Finance, Elsevier, vol. 49(2), pages 725-729, May.
    17. Mohamed A. Ayadi & Hatem Ben-Ameur & Nabil Channouf & Quang Khoi Tran, 2019. "NORTA for portfolio credit risk," Annals of Operations Research, Springer, vol. 281(1), pages 99-119, October.
    18. Dirk Tasche, 2005. "Measuring sectoral diversification in an asymptotic multi-factor framework," Papers physics/0505142, arXiv.org, revised Jul 2006.
    19. Paul Embrechts & Bin Wang & Ruodu Wang, 2015. "Aggregation-robustness and model uncertainty of regulatory risk measures," Finance and Stochastics, Springer, vol. 19(4), pages 763-790, October.
    20. Thierry Cohignac & Nabil Kazi-Tani, 2019. "Quantile Mixing and Model Uncertainty Measures," Working Papers hal-02405859, HAL.
    21. Embrechts, Paul & Puccetti, Giovanni & Rüschendorf, Ludger, 2013. "Model uncertainty and VaR aggregation," Journal of Banking & Finance, Elsevier, vol. 37(8), pages 2750-2764.

    More about this item

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies

    Statistics

    Access and download statistics

    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:ksa:szemle:1182. 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: Odon Sok (email available below). General contact details of provider: http://www.kszemle.hu .

    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.