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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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    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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