The document discusses systemic and systematic risk as addressed in various papers, particularly focusing on the implications for European sovereigns and financial firms. It critiques the distinctions and definitions of systemic risk, suggesting that systemic risk is broader than contagion risk and has significant real economic impacts. The text emphasizes the importance of using advanced models to identify macro-prudential measures and the need for informed policy interventions in the financial system.

1. Discussion of
“Systemic and Systematic risk” by Billio et al.
and
“CDS based indicators for systemic risk of
Euro area sovereigns and for Euro area
financial firms” by Lucas et al.
SYstemic Risk TOmography:
Signals, Measurements, Transmission Channels, and
Policy Interventions
Carsten Detken
Head of Financial Stability Surveillance Division
Directorate General Macroprudential Policy and Financial Stability
SYRTO Code Workshop
Workshop on Systemic Risk Policy Issues for SYRTO
July, 22014 - Frankfurt (Bundesbank-ECB-ESRB)

2. Carsten Detken
Head of Financial Stability Surveillance Division
Directorate General Macroprudential Policy and Financial Stability
Discussion of
“Systemic and Systematic risk” by Billio et al.
and
“CDS based indicators for systemic risk of Euro area sovereigns
and for Euro area financial firms” by Lucas et al.
2 July 2014 SYRTO workshop

3. RubricPapers by A. Lucas and co-authors
Empirical implications of the models consistent with economic intuition:
• Strong dependencies between European sovereigns
• In 2011 IE and PT perceived to be more affected by a possible Greek default than
others
 Concerns mostly about countries which are already fiscally weaker
• EFSF/SMP and OMT announcements decreased joint sovereign default risk but
not conditional risk/dependence (contagion)
 Arguably, this is what they were intended to: eliminate break-up risk
• Joint tail risks for EA banks peak in summer 2012
• OMT announcement decreased joint bank default risk but not conditional
risk/dependence (contagion)
 Owing to absence of full banking union
Praise

4. Rubric
Marginal value added?
Papers by A. Lucas and co-authors
Important for policy-making?
Criticism
Source: Lucas et al., JBES 2014
10-year sovereign bond yields for selected EA countries
Source: Reuters

5. Rubric
• One potential use of the approach
– Obtaining long time series of “systemic stress” e.g. for dating banking crises or
use in early warning models. Those measures would have to be based on
bond yields and/or equity prices which cover longer time spans (than CDS
spreads).
• Conditional probabilities of default
– Segoviano and Goodhart (2009): “conditional probabilities do not imply
causation”. But what to learn from those measures? Disentangling the drivers!
– Page 280 JBES: “During 2011 Ireland and Portugal are perceived by credit
market participants to be more affected by a possible Greek credit event”.
However, P(A) was the highest for PT and IE (after GR) at that time.

6. RubricPapers by A. Lucas and co-authors
Proposal
Real-time
Out-of-sample
A policy-maker's wish list:
General comments
• What is the meaning of now-casting in a model featuring daily data?
• Spatial dependence vs. spillover vs. financial (de)fragmentation?
Source: Blasques et al., 2014

7. Rubric
• Is spatial dependence necessarily bad?
• Given the separation in the EA (stressed vs. non-stressed countries) is it sufficient
to describe spatial dependence with a single parameter?
• Is the time-varying rho capturing/approximating a possibly higher order spatial
dependence process?
• What is the economic intuition for choosing h(ft)=tanh-1 as monotonic
transformation (equ. 4, p. 5 and p.17 eq. 17)?
• Why not normalise the exposure matrix W by GDP? Economically a time-
varying matrix seems more reasonable. Good fit is not everything. Would be
interesting to understand better why Wcat (p.21) performs best compared to others
(p. 25). Is it because the other regressors already pick up lots of volatility so that a
time-varying matrix at lower frequency doesn’t help much?
• See also comparisons of results with different W-matrices. Would be nice to
see the associated rho-measures.

8. Rubric
• It appears that the most important improvement comes from assuming t-
distributed errors instead for Gaussian. The improvement from using the GAS-
model instead of the static model is much smaller (Table 4, Appendix).
• P. 23: “Our results suggest that the sovereign CDS market had already
acknowledged the message earlier, resulting in an overall decrease of spatial
dependence”. What does this exactly mean in terms of timing? The Draghi
speech in London was the major trigger for reducing redenomination risk…
• P. 23. Next sentence on regulatory treatment. This is a smooth process, so
unlikely that it is the final recap implementation in mid-April would trigger a
downshift in spatial dependence. This is a bit of loose story telling.
• Marginal contribution of this approach against simple spread measures
(e.g. EURBOR-OIS) or the CISS (see Arsov et al. 2013)?

9. RubricBillio et al. (2014)
1. Can we separate systematic from systemic risk?
2. Does systemic risk kill diversification benefits?
Definition of both terms is crucial for answering the proposed question(s).
– “Systematic risk” is a well established concept.
• It refers to the risk an investor of a well-diversified portfolio is exposed to, which stems
from the dependence of returns to common factors.
• No market failure, no policy intervention necessary to prevent the problem.
– There is still no consensus on the definition of the term “Systemic risk”.
• ECB (FSR 6/2010): “[…] financial instability becomes so widespread that it
impairs the functioning of a financial system to the point where economic growth
and welfare suffer materially.”
• Systemic risk is associated with market failure.
• Volatility is not and bank failure not necessarily systemic,
Difference systemic – systematic risk

10. RubricBillio et al. (2014) cont’d
• Nice idea to link network analysis with time series/panel models.
However:
• According to Acharya and Yorulmazer (2002), Nier et al. (2007) and De
Bandt et al. (2010) systemic risk materialises through (1) “pure” contagion,
(2) exposure to common factors, (3) herding behaviour causing informational
contagion, and (4) feedback effects from endogenous fire sales. Hartmann
(2002) argues systemic risk stems from either build-up imbalances,
contagion or large shocks. This paper equates systemic risk with
contagion risk, which is too narrow. Final impact on real economy is key
defining element of systemic risk.
• Also need exposure based network analysis to conduct macro-pru policies.
But analysis can help investors (and supervisors) to evaluate level of risk
diversification in the presence of systemic risk.

11. Rubric
What are we concerned with now.....
• Prevention => When to use macro-pru instruments?
• Need models identifying vulnerable states to inform judgement
• - Lead times
• - Evaluation methods
• - Suite of models => Meta EWS?
• - Lack of Events
• - Thresholds from pooled analysis or country specific?
• - How to implement in policy process (intuition, performance,
visualisation)

12. This project has received funding from the European Union’s
Seventh Framework Programme for research, technological
development and demonstration under grant agreement n° 320270
www.syrtoproject.eu
This document reflects only the author’s views.
The European Union is not liable for any use that may be made of the information contained therein.