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Cascades in real interbank markets

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  • Fariba Karimi
  • Matthias Raddant

Abstract

We analyze cascades of defaults in an interbank loan market. The novel feature of this study is that the network structure and the size distribution of banks are derived from empirical data. We find that the ability of a defaulted institution to start a cascade depends on an interplay of shock size and connectivity. Further results indicate that the ability to limit default risk by spreading the lending to many counterparts decreased with the financial crisis. To evaluate the influence of the network structure on market stability, we compare the simulated cascades from the empirical network with results from different randomized network models. The results show that the empirical network has non-random features, which cannot be captured by rewired networks. The analysis also reveals that simulations assuming homogeneity for size of banks and loan contracts dramatically overestimates the fragility of the interbank market.

Suggested Citation

  • Fariba Karimi & Matthias Raddant, 2013. "Cascades in real interbank markets," Papers 1310.1634, arXiv.org, revised Dec 2014.
  • Handle: RePEc:arx:papers:1310.1634
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    3. Matteo Cinelli & Giovanna Ferraro & Antonio Iovanella & Giulia Rotundo, 2021. "Assessing the impact of incomplete information on the resilience of financial networks," Annals of Operations Research, Springer, vol. 299(1), pages 721-745, April.
    4. Lux, Thomas, 2016. "Network effects and systemic risk in the banking sector," FinMaP-Working Papers 62, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.
    5. Guidolin, Massimo & Pedio, Manuela, 2021. "Media Attention vs. Sentiment as Drivers of Conditional Volatility Predictions: An Application to Brexit," Finance Research Letters, Elsevier, vol. 42(C).
    6. Mitja Steinbacher & Matthias Raddant & Fariba Karimi & Eva Camacho Cuena & Simone Alfarano & Giulia Iori & Thomas Lux, 2021. "Advances in the agent-based modeling of economic and social behavior," SN Business & Economics, Springer, vol. 1(7), pages 1-24, July.
    7. Morteza Alaeddini & Philippe Madiès & Paul J. Reaidy & Julie Dugdale, 2023. "Interbank money market concerns and actors’ strategies—A systematic review of 21st century literature," Journal of Economic Surveys, Wiley Blackwell, vol. 37(2), pages 573-654, April.
    8. Hossein Dastkhan & Naser Shams Gharneh, 2019. "Simulation of Contagion in the Stock Markets Using Cross-Shareholding Networks: A Case from an Emerging Market," Computational Economics, Springer;Society for Computational Economics, vol. 53(3), pages 1071-1101, March.
    9. Ding Ding & Liyan Han & Libo Yin, 2017. "Systemic risk and dynamics of contagion: a duplex inter-bank network," Quantitative Finance, Taylor & Francis Journals, vol. 17(9), pages 1435-1445, September.
    10. V. Sasidevan & Nils Bertschinger, 2019. "Systemic Risk: Fire-Walling Financial Systems Using Network-Based Approaches," Papers 1912.05273, arXiv.org.
    11. Hossein Dastkhan, 2021. "Network‐based early warning system to predict financial crisis," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(1), pages 594-616, January.
    12. Ohsung Kwon & Sung-guan Yun & Seung Hun Han & Yang Hon Chung & Duk Hee Lee, 2018. "Network Topology and Systemically Important Firms in the Interfirm Credit Network," Computational Economics, Springer;Society for Computational Economics, vol. 51(4), pages 847-864, April.

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    JEL classification:

    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G01 - Financial Economics - - General - - - Financial Crises
    • E47 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Forecasting and Simulation: Models and Applications
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General

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