Systemic risk in Europe: Deciphering Leading Measures, Common Patterns and Real Effects

1. Systemic risk in Europe: Deciphering Leading Measures, Common Patterns and Real Effects Mikhail Stolbov Maria Shchepeleva MGIMO-University Bank of Russia 34th Symposium on Money, Banking and Finance Paris Nanterre University 5&6th July 2017

2. Outline • What is systemic risk? • Data and motivation for this research • Research questions • Leading measures of systemic risk • Commonality in the patterns of systemic risk • Real effects of systemic risk • Takeaway conclusions

3. What is systemic risk? • IMF-BIS-ECB (2009) definition The risk of threats to financial stability that impair the functioning of a large part of the financial system with significant adverse effects on the broader economy • Systemic risk has turned into a central category of macroprudential regulation, attracting particular attention since the Great Recession

4. Data and motivation for research • Over 30 competing measures of systemic risk (Bisias et al., 2012) • Few ready-to-use datasets with such indicators • Almost no datasets that aggregate measures at the country level • European Systemic Risk Dashboard (ESRD) and the HEC Lausanne Systemic Risk Dataset allow for such aggregation • The Lausanne dataset covers major non EU economies, e.g. Russia and Turkey • The Lausanne dataset includes important measures which are not monitored by the ESRD

5. Data and motivation for research • The long run marginal expected shortfall (LRMES) is defined as the sensitivity of capitalization of all domestic financial institutions to a 40% semiannual world stock market decline (Acharya and Brownlees, 2015) • The conditional capital shortfall measure (SRISK) captures the overall capital shortage which the financial institutions are to experience under the above-mentioned adverse conditions in the world market (Brownlees and Engle, 2017)

6. Data and motivation for research CS=kD+kW-W, CS - capital shortfall, D – value of debt, W – market value of equity, k – prudential ratio SRISK=kD+kW(1-LRMES)-W(1-LRMES) SRISK=kD-(1-k)W(1-LRMES)

7. Data and motivation for research Measures (monthly, January 2010-March 2016) • Long-run marginal expected shortfall (LRMES) • Conditional capital shortfall measure (SRISK) • Leverage ratio • Stock market capitalization (inverted value) • Volatility • Correlations with the world stock market

8. Data and motivation for research Guided by the recent research (Kritzman et al., 2011, Billio et al., 2012, Giglio et al., 2016), we construct two aggregate measures from the above-mentioned six, based on: principal component analysis independent component analysis, since ICA is reported to be superior to PCA in terms of non-Gaussian and nonlinear data (Hyvarinen, 2001)

9. Data and motivation for research 22 countries in the sample: 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.

10. Research questions • Which measure plays a pivotal role for each country? • Which factors underpin these systemic risk measures at the global, regional and national levels? • Are there any clusters of the European economies based on the underlying determinants? • Do these measures have any real effects?

11. Leading measures of systemic risk • We carry out an empirical horse race, comprising two pairwise Granger causality tests (standard, nonlinear (Diks and Panchenko, 2006)) and Bayesian VAR impulse-response functions (Litterman/Minnesota prior, with “from simple to aggregate” variable ordering) to determine leading measures • For every country ranks are assigned to each measure if it causes other measures (1) or is led by them (-1) • We sum up the ranks across the three tests to underscore leading measures

12. Leading measures of systemic risk

13. Leading measures of systemic risk • Volatility and SRISK are the most important metrics • Aggregate measures and LRMES perform quite modestly • Leverage tends to be the leading measure for bank-based economies, significantly dependent on external financing (the Netherlands, Russia, Spain, Turkey) • The standardized leading metrics exhibit co-movement, shaping constellations of systemic risk peaks (May 2010, September 2011, May 2012, etc.)

14. Leading measures of systemic risk

15. Commonality in the patterns of systemic risk • The standardized leading metrics are strongly connected (around 81% of total overlap in the sample), based on the cumulative Bhattacharya distances, a divergence-type measure for any two data distributions • The UK, Turkey and Germany are top-3 by the degree of connectedness, the rest (with a few exceptions) running relatively close to each other • The findings raise the question of common factors underlying the leading systemic risk measures

16. Commonality in the patterns of systemic risk Potential common drivers of the standardized leading measures (SYSRISKN) in the sample countries global: VIX index, TED spread, IMF commodity price index (COMPR) regional: composite index of systemic stress (CISS), VSTOXX national: interbank lending rates (INTBRATE) and long-term (10-year) bond yields (LTRATE)

17. Commonality in the patterns of systemic risk • We combine the standardized leading systemic risk measures and their potential drivers to estimate a panel vector autoregression model (PVAR) and run Granger causality tests based on it to find out which of the common drivers really drive systemic risk (have no causal feedback) • The VIX index, TED spread, CISS and long-term bond yields underpin the systemic risk measures

18. Commonality in the patterns of systemic risk

19. Commonality in the patterns of systemic risk • For each country we regress the standardized systemic risk measures on the VIX index, TED spread, CISS and bond yields in pairwise framework, using robust least squares (RLS) to account for outliers • Coefficients of determination from these regressions reveal the relative importance of these underlying determinants for national systemic risk • Based on them, we conduct cluster analysis to extract common patterns in the sample

20. Commonality in the patterns of systemic risk

21. Commonality in the patterns of systemic risk

22. Commonality in the patterns of systemic risk • Stopping rules (the Duda-Hart criteria and Calinski-Harabasz pseudo-F statistic) indicate the presence of 2 clusters (Germany, the Netherlands, France, Portugal, Ireland, Belgium and the rest) • For the fist cluster CISS and long-term bond yields are the major drivers, for the second all the four factors exert a commensurate impact • Interestingly, the PIIGS are split between the clusters

23. Real effects of systemic risk • We carry out Granger linear and nonlinear causality tests between the leading systemic risk measures and industrial production indices • There is more evidence for causality in the nonlinear tests than in linear ones • The tests give uniform results for Sweden (from systemic risk to IP) Hungary (from IP to systemic risk) Turkey (from IP to systemic risk) • Possibly bidirectional linkages for Spain and Cyprus • Inconclusive results for the rest

24. Takeaway conclusions • It is important for regulators to determine leading measures of systemic risk and we propose an empirical methodology for the Lausanne systemic risk dataset • Volatility, SRISK, followed by leverage, lead • Two country clusters are found within the sample, with CISS and long-term bond yields having the biggest discriminatory power • Systemic risk indeed has real effects but they do not seem omnipresent