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Systemic risk in Europe: deciphering leading measures, common patterns and real effects

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Abstract

The paper studies salient features of systemic risk in a sample of 22 European (EU and non-EU) countries during January 2010–March 2016. Building on a novel dataset and conducting an empirical horse race, we determine pivotal systemic risk measures for the sample countries. SRISK and volatility indicator tend to lead other metrics, followed by leverage. In contrast to the conventional wisdom, composite systemic risk measures aggregated with the aid of principal and independent component analysis perform worse. The leading systemic risk measures exhibit a high degree of connectedness. The VIX index, TED spread, the Composite Index of Systemic Stress (CISS) and long-term interest rates underlie their dynamics. Two clusters within the sample are identified, with CISS and long-term interest rates being crucial to distinguish between them. There is only scarce evidence for causal linkages between systemic risk and industrial production in the sample countries, based on the concurring results of standard and nonparametric Granger causality tests.

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Notes

  1. The sample includes Austria, Belgium, Cyprus, Denmark, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Luxembourg, the Netherlands, Norway, Poland, Portugal, Russia, Spain, Sweden, Switzerland, Turkey, the UK. Croatia, the Czech Republic, Malta, Romania, the Slovak Republic and the Ukraine are not considered because of missing data in one or more series.

  2. See the methodological note for more details http://www.crml.ch/index.php?id=44.

  3. See Hyvärinen et al. (2001) for a detailed description of the ICA theory. Our IC-based aggregate measures have been computed, using the MATLAB code available at the authors’ page: https://www.cs.helsinki.fi/u/ahyvarin/. In the fastICA algorithm, we select the hyperbolic tangent as the nonlinearity function and set the scale parameter equal to unity.

  4. In none of such countries in the sample (the Netherlands, Russia, Spain and Turkey), this macroprudential tool was introduced by 2017.

  5. We use a STATA routine provided by Prof. G.K. Brown to calculate the Bhattacharyya distances https://grahamkbrown.net/2015/10/08/bhatt/. We stick to the default number of partitions of the datasets, i.e. \(\hbox {N}=10\).

  6. They are available from the authors upon request.

  7. The detailed output of these tests is available from the authors upon request.

  8. We are led to this approach by Freixas et al. (2015) and Giglio et al. (2016) who argue that systemic risk measures are more informative about industrial production or other real activity indicators’ lower tails than about their central tendency. Unfortunately, due to the limited length of the time series, we are unable to exploit quantile regressions to assess an impact of the leading systemic risk measures on some lower percentile of industrial production in the sample countries.

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Authors and Affiliations

  1. Department of Applied Economics, Moscow State Institute of International Relations (MGIMO-University), 76 Vernadskogo Prospect, Moscow, Russia, 119454

    Mikhail Stolbov

  2. Department of Monetary Policy, Bank of Russia, Moscow, Russia

    Maria Shchepeleva

Authors
  1. Mikhail Stolbov
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  2. Maria Shchepeleva
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Corresponding author

Correspondence to Mikhail Stolbov.

Appendix

Appendix

See Tables 13, 14, 15 and 16.

Table 13 Results of bivariate Granger (no) causality tests
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Table 14 Results of nonparametric Granger (no) causality tests
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Table 15 Lead-lag patterns based on the IRF from BVAR models
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Table 16 Matrix of pairwise Bhattacharyya distances
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Stolbov, M., Shchepeleva, M. Systemic risk in Europe: deciphering leading measures, common patterns and real effects. Ann Finance 14, 49–91 (2018). https://doi.org/10.1007/s10436-017-0310-3

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