IDEAS home Printed from https://ideas.repec.org/p/pav/demwpp/085.html
   My bibliography  Save this paper

Identifying SIFI Determinants for Global Banks and Insurance Companies: Implications for D-SIFIs in Russia

Author

Listed:
  • Maiya Anokhina

    (National Research University Higher School of Economics, Moscow)

  • Henry Penikas

    () (National Research University Higher School of Economics, Moscow)

  • Victor Petrov

    (National Research University Higher School of Economics, Moscow)

Abstract

The increased role of financial institutions in the economy leads to a need to determine those that are systemically important. The bankruptcy of such institutions creates negative effects for the economy on the global scale. The aim of this article is to identify important financial coefficients that can be used in the methodology of identification of G-SIB and G-SII. Models of binary choice and models of ordered choice are used in this article, several models are highly predictive. Besides this paper has revealed several financial coefficients, that helped to find the probabilities of G-SIF for Russian banks and insurance companies.

Suggested Citation

  • Maiya Anokhina & Henry Penikas & Victor Petrov, 2014. "Identifying SIFI Determinants for Global Banks and Insurance Companies: Implications for D-SIFIs in Russia," DEM Working Papers Series 085, University of Pavia, Department of Economics and Management.
  • Handle: RePEc:pav:demwpp:085
    as

    Download full text from publisher

    File URL: http://dem-web.unipv.it/web/docs/dipeco/quad/ps/RePEc/pav/demwpp/DEMWP0085.pdf
    Download Restriction: no

    References listed on IDEAS

    as
    1. Beynon, Malcolm J. & Peel, Michael J., 2001. "Variable precision rough set theory and data discretisation: an application to corporate failure prediction," Omega, Elsevier, vol. 29(6), pages 561-576, December.
    2. Sinkey, Joseph F, Jr, 1975. "A Multivariate Statistical Analysis of the Characteristics of Problem Banks," Journal of Finance, American Finance Association, vol. 30(1), pages 21-36, March.
    3. Tabakis, Evangelos & Vinci, Anna, 2002. "Analysing and combining multiple credit assessments of financial institutions," Working Paper Series 0123, European Central Bank.
    4. repec:bla:joares:v:18:y:1980:i:1:p:109-131 is not listed on IDEAS
    5. Kolari, James & Glennon, Dennis & Shin, Hwan & Caputo, Michele, 2002. "Predicting large US commercial bank failures," Journal of Economics and Business, Elsevier, vol. 54(4), pages 361-387.
    6. Anatoly Peresetsky & Alexandr Karminsky & Sergei Golovan, 2011. "Probability of default models of Russian banks," Economic Change and Restructuring, Springer, vol. 44(4), pages 297-334, November.
    7. Kaufman, George G., 2002. "Too big to fail in banking: What remains?," The Quarterly Review of Economics and Finance, Elsevier, vol. 42(3), pages 423-436.
    8. Olmeda, Ignacio & Fernandez, Eugenio, 1997. "Hybrid Classifiers for Financial Multicriteria Decision Making: The Case of Bankruptcy Prediction," Computational Economics, Springer;Society for Computational Economics, vol. 10(4), pages 317-335, November.
    9. Bech, Morten L. & Chapman, James T.E. & Garratt, Rodney J., 2010. "Which bank is the "central" bank?," Journal of Monetary Economics, Elsevier, vol. 57(3), pages 352-363, April.
    10. Meyer, Paul A & Pifer, Howard W, 1970. "Prediction of Bank Failures," Journal of Finance, American Finance Association, vol. 25(4), pages 853-868, September.
    11. Alexander Karminsky & Alexander Kostrov, 2014. "The probability of default in Russian banking," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 4(1), pages 81-98, June.
    12. James B. Thomson, 1991. "Predicting bank failures in the 1980s," Economic Review, Federal Reserve Bank of Cleveland, issue Q I, pages 9-20.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    Systemic importance; Basel committee; probability of default; financial coefficients; models of ordered choice; models of binary choice; global systemically important banks (G-SIB); insurance company.;

    JEL classification:

    • C70 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - General
    • E58 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Central Banks and Their Policies
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:pav:demwpp:085. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Alice Albonico) The email address of this maintainer does not seem to be valid anymore. Please ask Alice Albonico to update the entry or send us the correct email address. General contact details of provider: http://edirc.repec.org/data/dppavit.html .

    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 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.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.