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Structure and Temporal Change of the Credit Network between Banks and Large Firms in Japan

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  • Iyetomi, Hiroshi
  • Ikeda, Yuichi
  • Aoyama, Hideaki
  • Fujiwara, Yoshi
  • Souma, Wataru
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    Abstract

    We present a new approach to understanding credit relationships between commercial banks and quoted firms, and with this approach, examine the temporal change in the structure of the Japanese credit network from 1980 to 2005. At each year, the credit network is regarded as a weighted bipartite graph where edges correspond to the relationships and weights refer to the amounts of loans. Reduction in the supply of credit affects firms as debtor, and failure of a firm influences banks as creditor. To quantify the dependency and influence between banks and firms, we propose a set of scores of banks and firms, which can be calculated by solving an eigenvalue problem determined by the weight of the credit network. We found that a few largest eigenvalues and corresponding eigenvectors are significant by using a null hypothesis of random bipartite graphs, and that the scores can quantitatively describe the stability or fragility of the credit network during the 25 years. --

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    File URL: http://dx.doi.org/10.5018/economics-ejournal.ja.2009-7
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    Bibliographic Info

    Article provided by Kiel Institute for the World Economy in its journal Economics: The Open-Access, Open-Assessment E-Journal.

    Volume (Year): 3 (2009)
    Issue (Month): 7 ()
    Pages: 1-18

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    Handle: RePEc:zbw:ifweej:7551

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    Related research

    Keywords: Banking; credit topology; bipartite network; systemic risk;

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    References

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    1. Kano, Masaji & Uchida, Hirofumi & Udell, Gregory F. & Watanabe, Wako, 2011. "Information verifiability, bank organization, bank competition and bank-borrower relationships," Journal of Banking & Finance, Elsevier, vol. 35(4), pages 935-954, April.
    2. Ogawa, Kazuo & Sterken, Elmer & Tokutsu, Ichiro, 2007. "Why do Japanese firms prefer multiple bank relationship? Some evidence from firm-level data," Economic Systems, Elsevier, vol. 31(1), pages 49-70, March.
    3. Masanao Aoki, . "Reconstructing Macroeconomics: A Perspective from Statistical Physics and Combinatorial Stochastic Processes," UCLA Economics Online Papers 390, UCLA Department of Economics.
    4. G. De Masi & Y. Fujiwara & M. Gallegati & B. Greenwald & J. E. Stiglitz, 2009. "An Analysis of the Japanese Credit Network," Papers 0901.2384, arXiv.org, revised Nov 2010.
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    Cited by:
    1. Alessandro Spelta & Tanya Araujo, 2012. "Interlinkages and structural changes in cross-border liabilities: a network approach," Working Papers Department of Economics 2012/19, ISEG - School of Economics and Management, Department of Economics, University of Lisbon.
    2. Bargigli, Leonardo & Gallegati, Mauro & Riccetti, Luca & Russo, Alberto, 2014. "Network analysis and calibration of the “leveraged network-based financial accelerator”," Journal of Economic Behavior & Organization, Elsevier, vol. 99(C), pages 109-125.
    3. Araújo, Tanya & Spelta, Alessandro, 2014. "Structural changes in cross-border liabilities: A multidimensional approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 394(C), pages 277-287.
    4. Delli Gatti, Domenico & Gallegati, Mauro & Greenwald, Bruce & Russo, Alberto & Stiglitz, Joseph E., 2010. "The financial accelerator in an evolving credit network," Journal of Economic Dynamics and Control, Elsevier, vol. 34(9), pages 1627-1650, September.
    5. Bargigli, Leonardo & Gallegati, Mauro, 2011. "Random digraphs with given expected degree sequences: A model for economic networks," Journal of Economic Behavior & Organization, Elsevier, vol. 78(3), pages 396-411, May.

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