Abstract
We investigate the information content of stock correlation based network measures for systemic risk rankings, such as SIFIRank (based on Google's PageRank). Using European banking data, we show that SIFIRank is empirically equivalent to a ranking based on average pairwise stock correlations as developed in this paper. The correlation based network measures complement currently available alternative systemic risk ranking methods based on book or market values. A further analytical investigation shows that the value-added appears to be mainly attributable to pairwise cross-sectional heterogeneity rather than to more subtle network relations and feedback loops.
| Original language | English |
|---|---|
| Pages (from-to) | 84-90 |
| Number of pages | 7 |
| Journal | Journal of Banking and Finance |
| Volume | 78 |
| Early online date | 10 Feb 2017 |
| DOIs | |
| Publication status | Published - May 2017 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 17 Partnerships for the Goals
Keywords
- European banking sector
- Network based risk measures
- Systemic risk ranking
- Systemically important financial institutions (SIFI)
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