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Key Borrowers Detection by Long-Range Interactions

Author

Listed:
  • Fuad Aleskerov

    (National Research University Higher School of Economics)

  • Natalia Meshcheryakova

    (National Research University Higher School of Economics)

  • Alisa Nikitina

    (National Research University Higher School of Economics)

  • Sergey Shvydun

    (National Research University Higher School of Economics)

Abstract

We propose a new method for assessing agents' influence in financial network structures, which takes into consideration the intensity of interactions. A distinctive feature of this approach is that it considers not only direct interactions of agents of the first level and indirect interactions of the second level, but also long-range indirect interactions. At the same time we take into account the attributes of agents as well as the possibility of impact to a single agent from a group of other agents. This approach helps us to identify systemically important elements which cannot be detected by classical centrality measures or other indices. The proposed method was used to analyze the banking foreign claims for the end of 1Q 2015. Under the approach, two types of key borrowers were detected: a) major players with high ratings and positive credit history; b) intermediary players, which have a great scale of financial activities through the organization of favorable investment conditions and positive business climate.

Suggested Citation

  • Fuad Aleskerov & Natalia Meshcheryakova & Alisa Nikitina & Sergey Shvydun, 2016. "Key Borrowers Detection by Long-Range Interactions," HSE Working papers WP BRP 56/FE/2016, National Research University Higher School of Economics.
  • Handle: RePEc:hig:wpaper:56/fe/2016
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    References listed on IDEAS

    as
    1. F. Aleskerov & N. Meshcheryakova & S. Shvydun, 2016. "Centrality measures in networks based on nodes attributes, long-range interactions and group influence," Papers 1610.05892, arXiv.org.
    2. Fuad Aleskerov, 2006. "Power Indices Taking into Account Agents’ Preferences," Studies in Choice and Welfare, in: Bruno Simeone & Friedrich Pukelsheim (ed.), Mathematics and Democracy, pages 1-18, Springer.
    3. Aleskerov, Fuad & Yakuba, Vyacheslav & Yuzbashev, Dmitriy, 2007. "A `threshold aggregation' of three-graded rankings," Mathematical Social Sciences, Elsevier, vol. 53(1), pages 106-110, January.
    4. Nicole Allenspach & Pierre Monnin, 2009. "International Integration, Common Exposure and Systemic Risk in the Banking Sector," World Scientific Book Chapters, in: Douglas D Evanoff & David S Hoelscher & George G Kaufman (ed.), Globalization And Systemic Risk, chapter 16, pages 233-249, World Scientific Publishing Co. Pte. Ltd..
    5. Aleskerov, Fuad & Chistyakov, Vyacheslav V. & Kalyagin, Valery, 2010. "The threshold aggregation," Economics Letters, Elsevier, vol. 107(2), pages 261-262, May.
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    Cited by:

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    More about this item

    Keywords

    systemic risk; key borrower; interconnectedness; centrality;
    All these keywords.

    JEL classification:

    • C7 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory
    • G2 - Financial Economics - - Financial Institutions and Services

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