 From liquidity risk to systemic risk: A use of knowledge graphRen-Raw Chen and Xiaohu Zhang Journal of Financial Stability, 2024, vol. 70, issue C 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. Keywords: Knowledge graph; Liquidity index; Systemic risk; Global crisis; Machine learning (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:finsta:v:70:y:2024:i:c:s1572308923000955 DOI: 10.1016/j.jfs.2023.101195 Access Statistics for this article Journal of Financial Stability is currently edited by I. Hasan, W. C. Hunter and G. G. Kaufman More articles in Journal of Financial Stability from Elsevier |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.