IDEAS home Printed from https://ideas.repec.org/a/taf/oaefxx/v5y2017i1p1311097.html
   My bibliography  Save this article

Modern claim frequency and claim severity models: An application to the Russian motor own damage insurance market

Author

Listed:
  • Evgenii V. Gilenko
  • Elena A. Mironova

Abstract

During 2012–2015, the motor insurance in Russia received considerable attention both from the parts of the Russian government and from the insurance business. This was caused, in particular, by significant losses from the side of insurance companies that occurred during 2012–2013. Experts explain these losses not only by the effects of inflation or by the changes in Russian insurance legislation, but also by the incomplete set of factors that has been used by insurance companies for tariff calculation. This research analyses the factors that influence claim frequency and claim severity in the Russian motor own damage (MOD) insurance to assess the efficiency of the existing set of factors used for MOD insurance tariff calculations. To this end, we employ the appropriate claim frequency and claim severity models on the data provided by one of the leading St. Petersburg (Russia) insurance companies for the period of 2012–2013. The results of our calculations, organized within a resampling framework, show that additional factors may indeed be worth taking into account in the MOD insurance tariff calculation.

Suggested Citation

  • Evgenii V. Gilenko & Elena A. Mironova, 2017. "Modern claim frequency and claim severity models: An application to the Russian motor own damage insurance market," Cogent Economics & Finance, Taylor & Francis Journals, vol. 5(1), pages 1311097-131, January.
    • Handle: RePEc:taf:oaefxx:v:5:y:2017:i:1:p:1311097
    DOI: 10.1080/23322039.2017.1311097
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/23322039.2017.1311097
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/23322039.2017.1311097?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
    ---><---

    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. Zeileis, Achim & Kleiber, Christian & Jackman, Simon, 2008. "Regression Models for Count Data in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i08).
    2. Mullahy, John, 1986. "Specification and testing of some modified count data models," Journal of Econometrics, Elsevier, vol. 33(3), pages 341-365, December.
    3. Frees, Edward W. & Valdez, Emiliano A., 2008. "Hierarchical Insurance Claims Modeling," Journal of the American Statistical Association, American Statistical Association, vol. 103(484), pages 1457-1469.
    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. Shengkun Xie, 2021. "Improving Explainability of Major Risk Factors in Artificial Neural Networks for Auto Insurance Rate Regulation," Risks, MDPI, vol. 9(7), pages 1-21, July.

    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. Christian Kleiber & Achim Zeileis, 2016. "Visualizing Count Data Regressions Using Rootograms," The American Statistician, Taylor & Francis Journals, vol. 70(3), pages 296-303, July.
    2. Livio Finos & Fortunato Pesarin, 2020. "On zero-inflated permutation testing and some related problems," Statistical Papers, Springer, vol. 61(5), pages 2157-2174, October.
    3. Andre Jungmittag, 2019. "Service trade restrictiveness and internationalisation of retail trade," International Economics and Economic Policy, Springer, vol. 16(2), pages 293-333, April.
    4. Christian Balcells, 2022. "Determinants of firm boundaries and organizational performance: an empirical investigation of the Chilean truck market," Journal of Evolutionary Economics, Springer, vol. 32(2), pages 423-461, April.
    5. Moritz Berger & Gerhard Tutz, 2021. "Transition models for count data: a flexible alternative to fixed distribution models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(4), pages 1259-1283, October.
    6. Mihaela Covrig & Iulian Mircea & Gheorghita Zbaganu & Alexandru Coser & Alexandru Tindeche, 2015. "Using R In Generalized Linear Models," Romanian Statistical Review, Romanian Statistical Review, vol. 63(3), pages 33-45, September.
    7. Filipe Sengo Furtado & Thomas Reutterer & Nadine Schröder, 2022. "The carrot and the stick in online reviews: determinants of un-/helpfulness voting choices," Journal of Business Economics, Springer, vol. 92(4), pages 565-590, May.
    8. Addisu Jember Zeleke & Serena Moscato & Rossella Miglio & Lorenzo Chiari, 2022. "Length of Stay Analysis of COVID-19 Hospitalizations Using a Count Regression Model and Quantile Regression: A Study in Bologna, Italy," IJERPH, MDPI, vol. 19(4), pages 1-18, February.
    9. A. Baccini & L. Barabesi & M. Cioni & C. Pisani, 2014. "Crossing the hurdle: the determinants of individual scientific performance," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(3), pages 2035-2062, December.
    10. Počuča, Nikola & Jevtić, Petar & McNicholas, Paul D. & Miljkovic, Tatjana, 2020. "Modeling frequency and severity of claims with the zero-inflated generalized cluster-weighted models," Insurance: Mathematics and Economics, Elsevier, vol. 94(C), pages 79-93.
    11. Thomas Wieland, 2023. "Spatial shopping behavior during the Corona pandemic: insights from a micro-econometric store choice model for consumer electronics and furniture retailing in Germany," Journal of Geographical Systems, Springer, vol. 25(2), pages 291-326, April.
    12. Binder, Stefan & Macfarlane, Gregory S. & Garrow, Laurie A. & Bierlaire, Michel, 2014. "Associations among household characteristics, vehicle characteristics and emissions failures: An application of targeted marketing data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 59(C), pages 122-133.
    13. Nan-Ting Liu & Feng-Chang Lin & Yu-Shan Shih, 2020. "Count regression trees," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 14(1), pages 5-27, March.
    14. Christian Pierdzioch & Eike Emrich, 2013. "A Note on Corruption and National Olympic Success," Atlantic Economic Journal, Springer;International Atlantic Economic Society, vol. 41(4), pages 405-411, December.
    15. Ayan CHOWDHURY & Soma Chowdhury BISWAS, 2018. "Appropriate statistical model for zero-inflated count data: simulation based study," Computational Methods in Social Sciences (CMSS), "Nicolae Titulescu" University of Bucharest, Faculty of Economic Sciences, vol. 6(1), pages 5-12, June.
    16. John Haslett & Andrew C. Parnell & John Hinde & Rafael de Andrade Moral, 2022. "Modelling Excess Zeros in Count Data: A New Perspective on Modelling Approaches," International Statistical Review, International Statistical Institute, vol. 90(2), pages 216-236, August.
    17. Zhichao Fang & Rodrigo Costas & Paul Wouters, 2022. "User engagement with scholarly tweets of scientific papers: a large-scale and cross-disciplinary analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(8), pages 4523-4546, August.
    18. Gozde Ozonder & Eric J. Miller, 2021. "Longitudinal analysis of activity generation in the Greater Toronto and Hamilton Area," Transportation, Springer, vol. 48(3), pages 1149-1183, June.
    19. Sarah Harris & Wendy Anderson & Musa Kilinc & Liam Fogarty, 2012. "The relationship between fire behaviour measures and community loss: an exploratory analysis for developing a bushfire severity scale," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 63(2), pages 391-415, September.
    20. Alberto Baccini & Lucio Barabesi & Martina Cioni & Caterina Pisani, 2013. "Crossing the hurdle: the determinants of individual scientific performance," Department of Economics University of Siena 691, Department of Economics, University of Siena.

    More about this item

    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:taf:oaefxx:v:5:y:2017:i:1:p:1311097. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/OAEF20 .

    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.