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From liquidity risk to systemic risk: A use of knowledge graph

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  • Chen, Ren-Raw
  • Zhang, Xiaohu

Abstract

In this paper, we use knowledge graph (KG) to study systemic risk in the banking industry. KG provides a graphic representation of the connections of entities of interest (known as vertices or nodes) with the strengths of connections being reflected by the lines connecting them (known as edges) or distances between them. As a result, KG is a natural tool for visualizing the relationships among financial institutions. Furthermore, various data and graph choices can present how differently entities of interest can be connected. In this paper, we draw KGs on two datasets: liquidity index and volatility and three different embedding methods: locally linear embedding, spectral embedding and principal component analysis. Our empirical results show, not surprisingly, that volatility and liquidity index are not similar in explaining how banks are connected. Embedding methods also matter.

Suggested Citation

  • Chen, Ren-Raw & Zhang, Xiaohu, 2024. "From liquidity risk to systemic risk: A use of knowledge graph," Journal of Financial Stability, Elsevier, vol. 70(C).
    • Handle: RePEc:eee:finsta:v:70:y:2024:i:c:s1572308923000955
    DOI: 10.1016/j.jfs.2023.101195
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    1. Deng, Yang & Zeng, Yan & Li, Zhirui, 2019. "Real estate prices and systemic banking crises," Economic Modelling, Elsevier, vol. 80(C), pages 111-120.
    2. Fabio Caccioli & Paolo Barucca & Teruyoshi Kobayashi, 2018. "Network models of financial systemic risk: a review," Journal of Computational Social Science, Springer, vol. 1(1), pages 81-114, January.
    3. Paolo Giudici & Laura Parisi, 2016. "CoRisk: measuring systemic risk through default probability contagion," DEM Working Papers Series 116, University of Pavia, Department of Economics and Management.
    4. Ralph S. J. Koijen & Motohiro Yogo, 2019. "A Demand System Approach to Asset Pricing," Journal of Political Economy, University of Chicago Press, vol. 127(4), pages 1475-1515.
    5. Ni Zhan & Yijia Sun & Aman Jakhar & He Liu, 2021. "Graphical Models for Financial Time Series and Portfolio Selection," Papers 2101.09214, arXiv.org.
    6. Ahelegbey, Daniel Felix, 2015. "The Econometrics of Bayesian Graphical Models: A Review With Financial Application," MPRA Paper 92634, University Library of Munich, Germany, revised 25 Apr 2016.
    7. Tabak, Benjamin M. & Takami, Marcelo & Rocha, Jadson M.C. & Cajueiro, Daniel O. & Souza, Sergio R.S., 2014. "Directed clustering coefficient as a measure of systemic risk in complex banking networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 394(C), pages 211-216.
    8. Nicola, Giancarlo & Cerchiello, Paola & Aste, Tomaso, 2020. "Information network modeling for U.S. banking systemic risk," LSE Research Online Documents on Economics 107563, London School of Economics and Political Science, LSE Library.
    9. Marina Dolfin & Damian Knopoff & Michele Limosani & Maria Gabriella Xibilia, 2019. "Credit Risk Contagion and Systemic Risk on Networks," Mathematics, MDPI, vol. 7(8), pages 1-16, August.
    10. Francesca Biagini & Jean-Pierre Fouque & Marco Frittelli & Thilo Meyer-Brandis, 2018. "On Fairness of Systemic Risk Measures," Papers 1803.09898, arXiv.org, revised Apr 2019.
    11. Elias G. Carayannis & David F. J. Campbell, 2019. "Smart Quintuple Helix Innovation Systems," SpringerBriefs in Business, Springer, number 978-3-030-01517-6, January.
    12. Varecha Daniel & Kohar Robert & Brumercik Frantisek, 2019. "AGV Brake System Simulation," LOGI – Scientific Journal on Transport and Logistics, Sciendo, vol. 10(1), pages 1-9, May.
    13. Wenwei Li & Shenglin Ben & Ulrich Hommel & Sandra Paterlini & Jiefang Yu, 2019. "Default contagion and systemic risk in loan guarantee networks," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 59(S2), pages 1923-1946, November.
    14. Rowland J. O. Ekeocha, 2019. "Optimization of Systems," International Journal of Sciences, Office ijSciences, vol. 8(03), pages 118-125, March.
    15. Michael Boss & Helmut Elsinger & Martin Summer & Stefan Thurner, 2004. "Network topology of the interbank market," Quantitative Finance, Taylor & Francis Journals, vol. 4(6), pages 677-684.
    16. Jiannan Yu & Jinlou Zhao, 2020. "Prediction of Systemic Risk Contagion Based on a Dynamic Complex Network Model Using Machine Learning Algorithm," Complexity, Hindawi, vol. 2020, pages 1-13, August.
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    Cited by:

    1. Armanious, Amir, 2024. "Too-systemic-to-fail: Empirical comparison of systemic risk measures in the Eurozone financial system," Journal of Financial Stability, Elsevier, vol. 73(C).

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    More about this item

    Keywords

    Knowledge graph; Liquidity index; Systemic risk; Global crisis; Machine learning;
    All these keywords.

    JEL classification:

    • G01 - Financial Economics - - General - - - Financial Crises
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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