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Networks and market-based measures of systemic risk: the European banking system in the aftermath of the financial crisis

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Abstract

This article investigates the behaviour of the European banking system during the financial crises that occurred in the last decades. Among the various approaches for measuring systemic risk, we consider network analysis, which describes the linkages among financial institutions and their whole structure. We construct a time-varying network of the European banking system. Banks are linked to form a global interconnected system and they mutually influence one another in terms of risk. We model their reciprocal influence via a weighted and directed network, in which weights are related to risk measures that are based on equity returns. Then, we apply two network indicators to investigate the prominence of a bank in spreading and receiving risk from the others. The results enable us to capture many features of the banking system while identifying the global systemically important banks. Moreover, the results of the analysis over time show how interconnections change over periods that are characterized by various economic scenarios.

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Notes

  1. For example, European Central Bank (2009) defines systemic risk as the “risk that financial instability becomes so widespread that it impairs the functioning of a financial system to the point where economic growth and welfare suffer materially”. Benoit et al. (2017) refer to the “risk that many market participants are simultaneously affected by severe losses, which then spread through the system”.

  2. We refer to the 2014 exercise since the sample is wider compared to the more recent exercises; our objective is to approximate the whole European banking system, including banks that differ in terms of size and features.

  3. The equity returns have been collected from FactSet financial data and analytics (www.factset.com).

  4. Density exceeds 0.99 in “2-2007” and “1-2008” and is equal to 1 at the end of 2008.

  5. This point will become more evident later via analysis of Fig. 2.

  6. See European Union Referendum Act, December 2015.

  7. For instance, between windows “2-2008” and “1-2009”, the 99th quantile of the MES distribution moves from 11% to approximately 14%, while the 99th quantile of the ES distribution varies from 19 to 25%.

  8. A smaller increase is observed for Permanent, for which the ES moves from 22 to 27%.

  9. As expected, the Brexit effect spreads over the whole banking system. For example, Raddant (2016) analyses the impact of Brexit on stock market prices and volatility in various sectors; all sectors in UK, Germany, France Spain and Italy react to the Brexit vote and the most pronounced effect is observable for the financial sector. Recently, Candelon et al. (2018) analysed the impact of Brexit on the European equity market by adopting a network approach that is based on a threshold vector autoregressive model and observed an increase in the degree of interconnectedness that was due to the Brexit event.

  10. The peak in 2008 of the network indicators is a common feature of networks that are based on equity returns. Similar results are observed for example, in Billio et al. (2012) and in Kenett et al. (2012); however, these works focus on mean correlations instead of tail dependence, which is the main driver of systemic risk.

  11. These data are available for large European banks on the EBA website.

  12. According to Benoit et al. (2018), the score of Nordea was lower than the cut-off value for being classified as a G-SIB in 2016. This bank was added to the list by regulatory judgement.

  13. Interconnections in the Basel score are quantified by intra-financial system assets and liabilities and securities outstanding, which are influenced by size.

  14. Related to this, Raddant (2016) shows that the Italian financial sector reacted more strongly to the Brexit vote compared to other European countries.

  15. The correlations with MES and DeltaCoVaR are insignificant, while the correlations with SRISK and DollarDeltaCoVaR (which account for size) are significant but not sufficiently strong to suggest overlapping information.

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Acknowledgements

We would like to thank the editor, the guest editors of the Special Issue “Taming financial systemic risk” and the anonymous referees for their careful reviews on a previous version of this paper.

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Authors and Affiliations

  1. Department of Mathematics, Finance and Econometrics, Università Cattolica del Sacro Cuore, Milan, Italy

    Gian Paolo Clemente

  2. Department of Statistics and Quantitative Methods, Università di Milano - Bicocca, Via Bicocca degli Arcimboldi, 8, 20126, Milan, Italy

    Rosanna Grassi & Chiara Pederzoli

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  1. Gian Paolo Clemente
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  2. Rosanna Grassi
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  3. Chiara Pederzoli
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Correspondence to Rosanna Grassi.

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Clemente, G.P., Grassi, R. & Pederzoli, C. Networks and market-based measures of systemic risk: the European banking system in the aftermath of the financial crisis. J Econ Interact Coord 15, 159–181 (2020). https://doi.org/10.1007/s11403-019-00247-4

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