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DebtRank Analysis of the Japanese Credit Network

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  • AOYAMA Hideaki
  • Stefano BATTISTON
  • FUJIWARA Yoshi

Abstract

We present an analysis of the lending/borrowing relationship between Japanese banks and Japanese firms, which form a bipartite credit network. We introduce distress to some initial node(s) (banks or firms) and allow it to propagate and contaminate other nodes in this network according to the relative exposure. First, by choosing the initial node to be a bank and taking the weighted average of the resulting distress distribution, with the weight proportional to the size (total assets) of each node, we identify the bank's importance to the whole network at the time of crisis. This leads to a nonlinear relationship between the importance and the size of the bank, which implies that mergers with the same-sized partner would result the most in the increase in importance. Second, by introducing the initial distress to firms in certain industrial sector(s), we evaluate the vulnerability of banks and firms in other sectors due to the distress in the initial sectors.

Suggested Citation

  • AOYAMA Hideaki & Stefano BATTISTON & FUJIWARA Yoshi, 2013. "DebtRank Analysis of the Japanese Credit Network," Discussion papers 13087, Research Institute of Economy, Trade and Industry (RIETI).
  • Handle: RePEc:eti:dpaper:13087
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    File URL: https://www.rieti.go.jp/jp/publications/dp/13e087.pdf
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    Cited by:

    1. Silva, Thiago Christiano & Souza, Sergio Rubens Stancato & Tabak, Benjamin Miranda, 2017. "Monitoring vulnerability and impact diffusion in financial networks," Journal of Economic Dynamics and Control, Elsevier, vol. 76(C), pages 109-135.
    2. Silva, Thiago Christiano & Tabak, Benjamin Miranda & Guerra, Solange Maria, 2017. "Why do vulnerability cycles matter in financial networks?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 592-606.
    3. Thiago Christiano Silva & Michel Alexandre da Silva & Benjamin Miranda Tabak, 2016. "Modeling Financial Networks: a feedback approach," Working Papers Series 438, Central Bank of Brazil, Research Department.
    4. Poledna, Sebastian & Bochmann, Olaf & Thurner, Stefan, 2017. "Basel III capital surcharges for G-SIBs are far less effective in managing systemic risk in comparison to network-based, systemic risk-dependent financial transaction taxes," Journal of Economic Dynamics and Control, Elsevier, vol. 77(C), pages 230-246.
    5. Stefano Battiston & Marco D'Errico & Stefano Gurciullo & Guido Caldarelli, 2015. "Leveraging the network: a stress-test framework based on DebtRank," Papers 1503.00621, arXiv.org, revised Feb 2016.

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