Regulation et risque systémique - Monica Billio. April, 7-11 2013. University of Orléans - Paris, France

1. Regulation
et
risque systémique
SYstemic Risk TOmography:
Signals, Measurements, Transmission Channels, and
Policy Interventions
M.Billio,Ca’Foscari University ofVenice (ITALY)
M.Getmansky, Isenberg School ofManagement, University of Massachusetts (USA)
D.Gray,International Monetary Fund (IMF)
A.W. Lo,MIT Sloan School of Management (USA)
R.C.Merton,MIT Sloan School ofManagement (USA)
L. Pelizzon,Ca'Foscari University ofVenice (Italy)andGoethe University Frankfurt (Germany)
University ofOrléans – Paris. November 5, 2013.

2. Sovereign, Bank, and Insurance Credit Spreads:
Connectedness and System Networks
M. Billio, M. Getmansky, D. Gray
A.W. Lo, R.C. Merton, L. Pelizzon
The research leading to these results has received funding from the European
Union, Inquire Europe, and Seventh Framework Programme FP7/2007-2013
under grant agreement SYRTO-SSH-2012-320270.
Funded by the European Union
7th Framework Programme (FP7)
SYRTO
1

3. Objectives
• The risks of the banking and insurance
systems have become increasingly
interconnected with sovereign risk
• Highlight interconnections:
• Among countries and financial
institutions
• Consider both explicit and implicit
connections
2

4. Methodology
• We propose to measure and analyze
interactions between banks, insurers,
and sovereigns using:
– Contingent claims analysis (CCA)
– Network approach
3

5. Background
• Existing methods of measuring financial stability
have been heavily criticized by Cihak (2007) and
Segoviano and Goodhart (2009):
• A good measure of systemic stability has to
incorporate two fundamental components:
– The probability of individual financial
institution or country defaults
– The probability and speed of possible shocks
spreading throughout the financial industry
and countries
4

6. Background
• Most policy efforts have not focused in a
comprehensive way on:
– Assessing network externalities
– Interconnectedness between financial institutions,
financial markets, and sovereign countries
– Effect of network and interconnectedness on
systemic risk
5

7. Background: Feedback Loops of Risk
from Explicit and Implicit Guarantees
Source: IMF GFSR 2010, October Dale Gray
6

8. Background
• The size, interconnectedness, and complexity of
individual financial institutions and their inter-
relationships with sovereign risk create
vulnerabilities to systemic risk
• We use Expected Loss Ratios (based on CCA) and
network measures to analyze financial system
interactions and systemic risk
7

9. Core Concept of CCA:
Merton Model
• Expected Loss Ratio (ELR)
= Cost of Guar/RF Debt = PUT/B exp[-rT]
• Fair Value CDS Spread = -log (1 – ELR)/ T
8

10. Moody’s KMV CreditEdge for
Banks and Insurers
• MKMV uses equity and equity volatility and default barrier (from
accounting information) to get “distance-to- distress” which it maps
to a default probability (EDF) using a pool of 30 years of default
information
• It then converts the EDF to a risk neutral default probability (RNDP)
using the market price of risk, then using the sector loss given default
(LGD) it calculates the Expected Loss Ratio (ELR) for banks and
Insurers:
EL Ratio = RNDP*LGD=PUT/B exp[-rT]
9

11. Sovereign Expected Loss Ratio
• For this study the formula for estimating sovereign EL is
simply derived from sovereign CDS
EL Ratio Sovereign = 1-exp(-(Sovereign CDS/10000)*T)
• EL ratios for both banks and sovereigns have a horizon of 5
years (5-year CDS most liquid)

12. Linear Granger Causality Tests
ELRk (t) = ak + bk ELRk(t-1) + bjk ELRj(t-1) + Ɛt
ELRj(t) = aj + bj ELRj(t-1) + bkj ELRk(t-1) + ζt
• If bjk is significantly > 0, then j influences k
• If bkj is significantly > 0, then k influences j
• If both are significantly > 0, then there is
feedback, mutual influence, between j and
k.
11

13. Data
• Sample: Jan 01-Mar12
• Monthly frequency
• Entities:
– 17 Sovereigns (10 EMU, 4 EU, CH, US, JA)
– 59 Banks (31EMU, 11EU, 2CH, 12US, 4JA)
– 42 Insurers (12EMU, 6EU, 16US, 2CH, 5CA)
• CCA - Moody’s KMV CreditEdge:
– Expected Loss Ratios (ELR)

14. Mar 12
Blue Insurance
Black Sovereign
Red Bank
Blue Insurance
Black Sovereign
Red Bank 13

15. Mar 12
Blue Insurance
Black Sovereign
Red Bank
Blue Insurance
Black Sovereign
Red Bank 14

16. Network Measures
• Degrees
• Connectivity
• Centrality
•Indegree (IN): number of incoming connections
•Outdegree (FROM): number of outgoing
connections
•Totdegree: Indegree + Outdegree
•Number of node connected: Number
of nodes reachable following the
directed path
•Average Shortest Path: The average
number of steps required to reach the
connected nodes
•Eigenvector Centrality (EC): The more the
node is connected to central nodes (nodes
with high EC) the more is central (higher
EC)

17. Network Measures:
FROM and TO Sovereign
17 X 102= 1734 potential connections FROM (idem for TO)
16

18. From GIIPS minus TO GIIPS
17

19. June 07
Blue Insurance
Black Sovereign
Red Bank
18

20. March 08
Blue Insurance
Black Sovereign
Red Bank
19

21. August 08
Greece
Blue Insurance
Black Sovereign
Red Bank
20

22. Spain
Blue Insurance
Black Sovereign
Red Bank
December 11
21

23. March 12US
Blue Insurance
Black Sovereign
Red Bank
IT

24. March 12
Blue Insurance
Black Sovereign
Red Bank
23

25. Causal Connections
TO
FROM
BAN
SOV-NON-
GIIPS
SOV-GIIPS INS
Jul04-Jun07
BAN 5.54% 0.69% 1.03% 2.13%
SOV-NG 6.72% 10.00% 8.00% 5.71%
SOV-G 2.07% 4.00% 20.00% 3.33%
INS 7.76% 6.90% 4.76% 5.05%
Sep05-Aug08
BAN 19.86% 10.70% 4.56% 19.67%
SOV-NG 20.00% 50.00% 28.00% 37.14%
SOV-G 30.18% 52.00% 55.00% 43.33%
INS 8.27% 6.43% 0.48% 14.92%
Jan09-Dec11
BAN 15.91% 5.06% 1.79% 11.65%
SOV-NG 29.91% 8.33% 3.33% 23.33%
SOV-G 32.50% 23.33% 5.00% 14.00%
INS 14.15% 3.96% 0.00% 11.79%
Apr09-Mar12
BAN 13.93% 3.27% 8.93% 7.46%
SOV-NG 11.31% 6.82% 8.33% 9.58%
SOV-G 25.00% 13.33% 0.00% 21.50%
INS 11.79% 1.04% 2.50% 7.88%
24

26. EU Banks Affecting EU Insurers
25

27. EU Insurers Affecting EU Banks
26

28. EU Insurers Affecting US Banks
27

29. US Insurers Affecting US Banks
28

30. Early Warning Signals
0
2000
4000
6000
8000
10000
12000
14000
0
1000000
2000000
3000000
4000000
5000000
6000000
7000000
8000000
9000000
10000000
Jan01_Dec03
Apr01_Mar04
Jul01_Jun04
Oct01_Sep04
Jan02_Dec04
Apr02_Mar05
Jul02_Jun05
Oct02_Sep05
Jan03_Dec05
Apr03_Mar06
Jul03_Jun06
Oct03_Sep06
Jan04_Dec06
Apr04_Mar07
Jul04_Jun07
Oct04_Sep07
Jan05_Dec07
Apr05_Mar08
Jul05_Jun08
Oct05_Sep08
Jan06_Dec08
Apr06_Mar09
Jul06_Jun09
Oct06_Sep09
Jan07_Dec09
Apr07_Mar10
Jul07_Jun10
Oct07_Sep10
Jan08_Dec10
Apr08_Mar11
Jul08_Jun11
Oct08_Sep11
Jan09_Dec11
Apr09_Mar12
EL # of lines
forecast
forecast
29

31. t=March 2008; t+1=March 2009; t = Jul 2011; t+1= Feb 2012
Cumulated Exp. Loss Ratio ≡ Expected Loss Ratio of institution i +
Expected Loss Ratios of institutions caused by i
Early Warning Signals
Cumulative Expected Loss
Ratios
March 09 February 12
Coeff t-stat Coeff t-stat
# of out lines 0.42 2.92
Closeness Centrality -0.63 -2.51 -0.96 -6.40
R-Square 0.17 0.24
30

32. Conclusion
• The system of banks, insurance companies,
and countries in our sample is highly
dynamically connected
• We show how one sovereign/financial
institution is spreading risk to another
sovereign/financial institution
• Network measures allow for early warnings
and assessment of the system complexity
31

33. Implications
• The decision to bail out a bank or sovereign
affects not only the sovereign and its own
banks but also other sovereigns and foreign
banks in a significant way
• Stress tests are not adequate. Need to
account for interconnectedness and non-
linearity in exposures
32

34. Thank You!
33

35. 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