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, Seventh Framework Programme FP7/2007-2013 under
grant agreement SYRTO-SSH-2012-320270.
7th Framework Programme (FP7)
Funded by the European Union
SYRTO
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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
• Quantify the effects of:
• Asset-liability mismatches within and across
countries and financial institutions
2
Methodology
• We propose to measure and analyze
interactions between financial
institutions, sovereigns using:
– Contingent claims analysis (CCA)
– Network approach
3
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 industry and
countries
4
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
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Background: Feedback Loops of Risk
from Explicit and Implicit Guarantees
Source: IMF GFSR 2010, October
Dale Gray
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Background
• The size, interconnectedness, and complexity of
individual financial institutions and their interrelationships with sovereign risk create
vulnerabilities to systemic risk
• We propose Expected Loss Ratios (based on CCA)
and network measures to analyze financial
system interactions and systemic risk
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Core Concept of CCA:
Merton Model
• Expected Loss Ratio = Cost of Guar/RF Debt
= PUT/B exp[-rT]
= ELR
• Fair Value CDS Spread = -log (1 – ELR)/ T
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Moody’s KMV CreditEdge for Banks and
Insurance Companies
• 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 (using the
market price of risk), then using the sector loss given default (LGD) it
calculates the Expected Loss Ratio (EL) for banks and Insurances:
EL Ratio = RNDP*LGDSector
• It calculates the Fair Value CDS Spread
Fair Value CDS Spread = -1/T ln (1 – EL Ratio)
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Why EL Values?
• EL Values are used because they do not have the
distortions which affect observed CDS Spreads
• For banks and some other financial institutions:
• The fair-value CDS spreads (implied credit spreads
derived from CCA models, i.e. derived from equity
information) are frequently > than the observed
market CDS
• This is due to the depressing effect of implicit and
explicit government guarantees
Why EL Values?
• In other cases, e.g. in the Euro area periphery countries,
bank and insurance company CDS appear to be affected
by spillover from high sovereign spreads (observed CDS
> FVCDS).
• For these reasons we use the EL associated with the
FVCDS spreads for banks and insurance companies
which do not contain the distortions of sovereign
guarantees or sovereign credit risk spillovers
Sovereign Expected Loss Ratio
• CCA has been applied to sovereigns, both emerging market and
developed sovereigns
• Sovereign CDS spreads can be modeled from sovereign CCA models
where the spread is associated with the expected loss value and
sovereign default barrier
• 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)
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.
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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 Insurance Companies (12EMU, 6EU, 16US,
2CH, 5CA)
• CCA - Moody’s KMV CreditEdge:
– Expected Loss (EL)
Mar 12
BlueInsurance
Insurance
Blue
BlackSovereign
Sovereign
Black
RedBank
Bank
Red
15
Mar 12
BlueInsurance
Insurance
Blue
BlackSovereign
Sovereign
Black
RedBank
Bank
Red
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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)
Network Measures:
FROM and TO Sovereign
17 X 102= 1734 potential connections FROM (idem for TO)
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From GIIPS minus TO GIIPS
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June 07
Blue Insurance
Black Sovereign
Red Bank
20
March 08
Blue Insurance
Black Sovereign
Red Bank
21
August 08
Blue Insurance
Black Sovereign
Red Bank
Greece
22
December 11
Spain
Blue Insurance
Black Sovereign
Red Bank
23
March 12
US
Blue Insurance
Black Sovereign
Red Bank
IT
March 12
Blue Insurance
Black Sovereign
Red Bank
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EL
Apr09_Mar12
Jan09_Dec11
6000000
Oct08_Sep11
Jul08_Jun11
Apr08_Mar11
Jan08_Dec10
Oct07_Sep10
Jul07_Jun10
Apr07_Mar10
Jan07_Dec09
Oct06_Sep09
Jul06_Jun09
Apr06_Mar09
Jan06_Dec08
Oct05_Sep08
Jul05_Jun08
8000000
Apr05_Mar08
Jan05_Dec07
Oct04_Sep07
Jul04_Jun07
Apr04_Mar07
Jan04_Dec06
Oct03_Sep06
Jul03_Jun06
Apr03_Mar06
Jan03_Dec05
Oct02_Sep05
Jul02_Jun05
Apr02_Mar05
Jan02_Dec04
Oct01_Sep04
Jul01_Jun04
Apr01_Mar04
Jan01_Dec03
Early Warning Signals
10000000
14000
9000000
forecast
12000
7000000
10000
forecast
8000
5000000
4000000
6000
3000000
4000
2000000
1000000
2000
0
0
# of lines
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Early Warning Signals
Cumulative losses
March 09
February 12
Coeff t-stat R-square
Coeff t-stat R-square
# of in line
# of out lines
# of lines
Closeness Centrality
Eigenvector
Centrality
0.40
2.92
-0.63 -2.51
0.17
0.23
0.87
-0.15
2.2
3.5
-7.0
-0.15
-4.4
0.42
t=March 2008 t+1=March 2009; t = Jul 2011; t+1= Feb 2012
Cumulated Exp. Loss ≡ Expected Loss of institution i + Expected losses of
institutions caused by i
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CDS data
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Comparison CDS-KMV
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Comparison CDS-KMV
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CDS: Dec 11
Spain
Blue Insurance
Black Sovereign
Red Bank
31
Dec 11 : EL-KMV
Blue Insurance
Black Sovereign
Red Bank
Spain
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CDS:Mar 12
Blue Insurance
Black Sovereign
Red Bank
IT
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US
Mar 12:EL-KMV
Blue Insurance
Black Sovereign
Red Bank
IT
Conclusion
• The system of banks, insurance companies,
and countries in our sample is highly
dynamically connected
• Insurance companies are becoming highly
connected…
• We show how one country is spreading risk to
another sovereign
• Network measures allow for early warnings
and assessment of the system complexity
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Thank You!
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