Abstract
Large exposure rules are considered critical for financial institutions, as they directly restrict the lending activity of banks to clients. However, empirical evidence suggests that those rules are difficult both for regulators to enforce and for financial institutions to implement. We present a data-driven analytical model that automatically and algorithmically creates groups of related parties based on ownership information, financial dependencies, business associations, and family ties. We develop a methodology based on linear algebra and networks to group clients, highlight missing critical information, and identify unreported business partners. The approach can be used both prospectively by banking institutions analyzing credit risk and by regulators. We include a case study, applying the methodology retrospectively to highlight large exposure violations and systemic risk leading up to the 2008 banking crises in Iceland.
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Notes
What constitutes a “significant” share is context-dependent, but s needs to be large enough to enable influence on the governance of the company.
The underlying ownership network is dense, and using more expanded group definitions resulted in graphs covering very large parts of the economy.
The exact value of the MUO is up for debate. For this example, we select an MUO of 20 % based on Icelandic Act No. 161/2002 on financial undertaking, which defines two parties to “act in concert if ... [a] party directly or indirectly owns at least 20 % of the voting rights in the company concerned.”
Baugur applied for bankruptcy protection in February of 2009.
They later abandoned this plan, nationalizing Glitnir instead.
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Appendix: Details of group size comparisons
Appendix: Details of group size comparisons
See Table 3.
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Benediktsdóttir, S., Bjarnadóttir, M.V. & Hansen, G.A. Large exposure estimation through automatic business group identification. Ann Oper Res 247, 503–521 (2016). https://doi.org/10.1007/s10479-015-1952-z
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DOI: https://doi.org/10.1007/s10479-015-1952-z