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Key Borrowers Detected by the Intensities of Their Short-Range Interactions

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Book cover Models, Algorithms and Technologies for Network Analysis (NET 2014)

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Abstract

The issue of systemic importance has received particular attention since the recent financial crisis when it came to the fore that an individual financial institution can disturb the whole financial system. Interconnectedness is considered as one of the key drivers of systemic importance. Several measures have been proposed in the literature in order to estimate the interconnectedness of financial institutions and systems. However, they do not fully take into consideration an important dimension of this characteristic: intensities of agents’ interactions. This paper proposes a novel method that solves this issue. Our approach is based on the power index and centrality analysis and is employed to find a key borrower in a loan market. To illustrate the feasibility of our methodology we apply it at the European Union level and find key countries-borrowers.

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Notes

  1. 1.

    For detailed literature review with respect to theoretical and empirical application of network approach to financial systems see [6, 32].

  2. 2.

    Details on these measures are provided in Sect. 3.

  3. 3.

    If the borrower is not pivotal in a distressing group, no intensities of connections are calculated for this borrower. The intensities are assumed to be zero for this borrower in this distressing group. The zero value is then taken into consideration for the calculation of the key borrower index for this borrower.

  4. 4.

    The logic behind this measure is well explained in [15].

  5. 5.

    We estimate only the outgoing Bonacich centrality in order to rank the borrowers (not the lenders).

  6. 6.

    Consolidated banking statistics, table 9D “Foreign claims by nationality of reporting banks, ultimate risk basis” http://www.bis.org/statistics/consstats.htm.

  7. 7.

    For an individual banking system analysis the quota should be set specifically for each bank as 25 % of its capital following the recommendations of the Basel Committee [10]. At the country level we likewise consider the same level of the quota. The results are similar also for higher levels of the quota.

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Acknowledgements

We are grateful to Professor Hasan Ersel from the Sabancı University (Turkey) for his valuable comments and to Vyacheslav Yakuba from the Institute of Control Sciences of the Russian Academy of Sciences for providing us the software. The work was supported by the International Laboratory of Decision Choice and Analysis (HSE) and by the International Laboratory for Institutional Analysis of Economic Reforms (HSE).

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Authors and Affiliations

  1. DeCAn Lab, Institute of Control Sciences of Russian Academy of Sciences, National Research University (NRU) Higher School of Economics, Moscow, Russia

    Fuad Aleskerov

  2. LIA Lab, National Research University (NRU) Higher School of Economics, Moscow, Russia

    Irina Andrievskaya

  3. National Research University (NRU) Higher School of Economics, Moscow, Russia

    Elena Permjakova

Authors
  1. Fuad Aleskerov
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  2. Irina Andrievskaya
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  3. Elena Permjakova
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Corresponding author

Correspondence to Irina Andrievskaya .

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Editors and Affiliations

  1. Laboratory of Algorithms and Technologies for Network Analysis (LATNA), National Research University Higher School of Economics, Nizhny Novgorod, Russia

    Valery A. Kalyagin

  2. Laboratory of Algorithms and Technologies for Network Analysis (LATNA), National Research University Higher School of Economics, Nizhny Novgorod, Russia

    Petr A. Koldanov

  3. Department of Industrial and System Engineering, Center for Applied Optimization University of Florida, Gainesville, Florida, USA

    Panos M. Pardalos

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Aleskerov, F., Andrievskaya, I., Permjakova, E. (2016). Key Borrowers Detected by the Intensities of Their Short-Range Interactions. In: Kalyagin, V., Koldanov, P., Pardalos, P. (eds) Models, Algorithms and Technologies for Network Analysis. NET 2014. Springer Proceedings in Mathematics & Statistics, vol 156. Springer, Cham. https://doi.org/10.1007/978-3-319-29608-1_18

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