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Dynamic Analysis of the Behavioural Patterns of the Largest Commercial Banks in the Russian Federation

Author

Listed:
  • Fuad Aleskerov
  • V. Belousova
  • M. Serdyuk
  • V. Solodkov

Abstract

This paper presents a pattern behavio ral analysis of 100 largest Russian commercial banks by total assets during an eight- year period: from the first quarter of 1999 to the second quarter of 2007. Bank performance indicators are analyzed. Structural similarities in the development of the banks are examined. A cluster analysis is applied to determine banks with a similar structure of operations. This analysis allows to estimate how the structure of the Russian banking system has been changing over time. In particular, it allows to identify prevailing patterns in the behavior of Russian commercial banks and to analyze the stability of their position in a particular pattern.

Suggested Citation

  • Fuad Aleskerov & V. Belousova & M. Serdyuk & V. Solodkov, 2008. "Dynamic Analysis of the Behavioural Patterns of the Largest Commercial Banks in the Russian Federation," ICER Working Papers - Applied Mathematics Series 12-2008, ICER - International Centre for Economic Research.
    • Handle: RePEc:icr:wpmath:12-2008
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    File URL: http://www.bemservizi.unito.it/repec/icr/wp2008/ICERwp12-08.pdf
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    References listed on IDEAS

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    1. Алескеров Ф. Т. & Солодков В. М. & Челнокова Д. С., 2006. "Динамический Анализ Паттернов Поведения Коммерческих Банков России," Higher School of Economics Economic Journal Экономический журнал Высшей школы экономики, CyberLeninka;Федеральное государственное автономное образовательное учреждение высшего образования «Национальный исследовательский университет «Высшая школа экономики», vol. 10(1), pages 48-62.
    2. Obeua S. Persons, 1999. "Using Financial Information to Differentiate Failed vs. Surviving Finance Companies in Thailand: An Implication for Emerging Economies," Multinational Finance Journal, Multinational Finance Journal, vol. 3(2), pages 127-145, June.
    3. Fuad Aleskerov & C. Emre Alper, 2000. "A Clustering Approach to Some Monetary Facts: A Long-Run Analysis of Cross-Country Data," The Japanese Economic Review, Japanese Economic Association, vol. 51(4), pages 555-567, December.
    4. Caner S. & Kontorovich V., 2004. "Efficiency of the banking sector in the Russian Federation with international comparison," Higher School of Economics Economic Journal Экономический журнал Высшей школы экономики, CyberLeninka;Федеральное государственное автономное образовательное учреждение высшего образования «Национальный исследовательский университет «Высшая школа экономики», vol. 8(3), pages 357-375.
    5. Alexis Derviz & JiÅí Podpiera, 2008. "Predicting Bank CAMELS and S&P Ratings: The Case of the Czech Republic," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 44(1), pages 117-130, January.
    6. Cole, Rebel A. & Gunther, Jeffery W., 1995. "A CAMEL rating's shelf life," MPRA Paper 24693, University Library of Munich, Germany, revised 01 Nov 2008.
    7. Otchere, Isaac & Chan, Janus, 2003. "Intra-industry effects of bank privatization: A clinical analysis of the privatization of the Commonwealth Bank of Australia," Journal of Banking & Finance, Elsevier, vol. 27(5), pages 949-975, May.
    8. James B. Thomson, 1991. "Predicting bank failures in the 1980s," Economic Review, Federal Reserve Bank of Cleveland, vol. 27(Q I), pages 9-20.
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    Cited by:

    1. Aysan, Ahmet Faruk & Ertek, Gurdal & Ozturk, Secil, 2009. "Assessing the adverse effects of interbank funds on bank efficiency through using semiparametric and nonparametric methods," MPRA Paper 38113, University Library of Munich, Germany.
    2. Ashill, Nicholas J. & Rod, Michel & Gibbs, Tanya, 2015. "Coping with stress: A study of retail banking service workers in Russia," Journal of Retailing and Consumer Services, Elsevier, vol. 23(C), pages 58-69.
    3. Белоусова В. Ю., 2009. "Эффективность Издержек Однородных Российских Коммерческих Банков: Обзор Проблемы И Новые Результаты," Higher School of Economics Economic Journal Экономический журнал Высшей школы экономики, CyberLeninka;Федеральное государственное автономное образовательное учреждение высшего образования «Национальный исследовательский университет «Высшая школа экономики», vol. 13(4), pages 489-519.

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    More about this item

    Keywords

    Bank; dynamic pattern analysis; cluster analysis;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • O16 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Financial Markets; Saving and Capital Investment; Corporate Finance and Governance

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