The document discusses systemic risk indicators and the challenges in measuring, monitoring, and managing systemic risk in the financial sector. It highlights the types of systemic risk, various measurement approaches, and ongoing research to improve risk assessment using market prices, network structures, and text parsing. The findings suggest that while current methods are promising, the data demands and complexities involved mean accurate statistical risk monitoring remains a challenge.

1. SYRTO/LABEX ReFi
Closing Conference
Paris, February 2016
Joint work with:
István Barra (a)
Francisco Blasques (a)
Siem Jan Koopman (a,b)
Rutger Jan Lange (a)
Michiel van de Leur (a)
Rutger Lit (a)
Federico Nucera (c)
Julia Schaumburg (a)
Bernd Schwaab (d,*)
Arjen Siegmann (a)
Xin Zhang (e,*)
a)Vrije Universiteit Amsterdam and Tinbergen Institute
b)CREATES
c) Luiss, Rome
d) ECB, these are not the opinions of the ECB
e) Riksbank, these are not the opinions of the Riksbank
André Lucas
Systemic Risk Indicators
SYstemic
Risk
TOmography:
Signals,
Measurements,
Transmission
Channels,
and
Policy
Interven@ons

2. (Gerlach,
2009:
policy
note
to
European
Parliament)
Financial
surveillance
before
the
current
crisis
erupted
suggested
that
problems
were
forming
but
the
indica@ons
were
too
imprecise
to
permit
a
policy
response.
Work
is
currently
being
undertaken
to
improve
the
measurement,
monitoring
and
management
of
systemic
risk.
That
requires
it
to
be
defined,
which
is
unproblema5c,
and
opera5onalized,
which
is
not.
While
promising
methods
to
measure
risk
exist,
the
data
demands
are
so
pronounced
that
sta5s5cal
risk
monitoring
will
remain
an
imprecise
science
for
years
to
come.
Where
are
we
now
?

3. Types
of
systemic
risk
• Level
of
systemic
risk
– is
systemic
risk
currently
high
or
low:
``objec@ve’’
policy
trigger
• Dynamics
of
systemic
risk
– is
systemic
risk
building
up
or
not,
growing
misalignments,
bubbles,
growing
linkages
• Distribu@on
of
systemic
risk
– finding
biggest
systemic
risk
contributors,
targeNed
monitoring

4. Types
of
measurements
• Market
prices
– forward
looking
(stock
markets,
yields,
CDS),
…
– but
also
possibly
misaligned
risk
cycles
(Minksy)
– signals
typically
coincidental

5. ESRB
Risk
Dashboard
(2016)
Lucas,
Schwaab,
Zhang
(2014,
SYRTO/JBES):
Condi@onal
euro
area
sovereign
default
risk
Lucas,
Schwaab,
Zhang
(2016,
SYRTO/JAppEctr):
Measuring
Credit
Risk
in
a
Large
Banking
System:
Econometric
Modeling
and
Empirics

6. Lange,
Siegmann
(2016,
SYRTO):
Es@ma@ng
Sovereign
Join
Default
Probabili@es
from
Bond
Yields

7. Types
of
measurements
• Fundamentals
versus
experience,
or
versus
prices
– create
a
benchmark
(fundamental)
and
see
whether
data
are
aligned
with
the
fundamentals
– typically
more
leading

8. Creal,
Schwaab,
Koopman,
Lucas
(2014,
SYRTO/REStat):
Observa@on
Driven
Mixed-­‐
Measurement
Dynamic
Factor
Models
with
an
Applica@on
to
Credit
Risk
Koopman,
Lucas,
Schwaab
(2014,
SYRTO/IJF):
Nowcas@ng
and
forecas@ng
global
financial
sector
stress
and
credit
market
disloca@on

9. Creal,
Schwaab,
Koopman,
Lucas
(2014,
SYRTO/REStat):
Observa@on
Driven
Mixed-­‐
Measurement
Dynamic
Factor
Models
with
an
Applica@on
to
Credit
Risk
Koopman,
Lucas,
Schwaab
(2014,
SYRTO/IJF):
Nowcas@ng
and
forecas@ng
global
financial
sector
stress
and
credit
market
disloca@on

10. Schwaab,
Koopman,
Lucas
(2016,
SYRTO/JAppEctr):
Global
Credit
Risk:
World,
Country
and
Industry
Factors

11. ESRB
Risk
Dashboard
(2016)
Schwaab,
Koopman,
Lucas
(2016,
SYRTO/JAppEctr):
Global
Credit
Risk:
World,
Country
and
Industry
Factors
Creal,
Schwaab,
Koopman,
Lucas
(2014,
SYRTO/REStat):
Observa@on
Driven
Mixed-­‐
Measurement
Dynamic
Factor
Models
with
an
Applica@on
to
Credit
Risk
Koopman,
Lucas,
Schwaab
(2014,
SYRTO/IJF):
Nowcas@ng
and
forecas@ng
global
financial
sector
stress
and
credit
market
disloca@on

12. Barra,
Lucas
(2016,
SYRTO):
Unobserved
components
in
corporate
defaults
and
bond
prices

13. Nucera,
Schwaab,
Koopman,
Lucas
(2016,
SYRTO/JEmpFin):
The
Informa@on
in
Systemic
Risk
Rankings

14. Koopman,
Lit,
Lucas
(2016,
SYRTO):
A
decomposi@on
of
economic
and
financial
@me
series
into
business
and
financial
cycles

15. Types
of
measurements
• Network
structures
– create
summary
measures
of
the
network
structure
– leading
or
coincidental?
– value-­‐added
to
macro
summaries?

16. van
de
Leur,
Lucas
(2016,
SYRTO):
Network,
Market,
and
Book-­‐Based
Systemic
Risk
Rankings.

17. van
de
Leur,
Lucas
(2016,
SYRTO):
Network,
Market,
and
Book-­‐Based
Systemic
Risk
Rankings.

18. van
de
Leur,
Lucas
(2016,
SYRTO):
Network,
Market,
and
Book-­‐Based
Systemic
Risk
Rankings.

19. Types
of
measurements
• Text
parsing
– count
posi@ve
and
nega@ve
news
– news
on
linkages,
even
if
indirect
?

20. Garmaev,
Rus@ge,
Lammers,
Borovkova
(2016,
VU):
Systemic
Risk:
A
News
Sen@ment
based
Approach
SRisk
SensR

21. Summary
and
conclusions
• Price
based
informa@on
largely
coincidental
• Misalignments
more
promising
in
lead
@mes,
though
also
more
data/methodology
intensive
• Network
data
appear
to
add
new
informa@on:
which?
And
how
useful?
• Research
direc@ons
– beNer
understanding
of
the
genesis
of
risks
and
imbalances;
find
appropriate
proxies
– exploi@ng
new
network
data
(benchmarking
will
be
hard)
– exploi@ng
text
or
other
big
data
sources
– measuring
and
exploi@ng
misalignments

22. This project has received funding from the European Union’s
Seventh Framework Programme for research, technological
development and demonstration under grant agreement no° 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.