sovereign, bank, and insurance credit spreads: connectedness and system networks
DESCRIPTION
7th Framework Programme (FP7) SYRTO. Funded by the European Union. Sovereign, Bank, and Insurance Credit Spreads: Connectedness and System Networks. M. Billio, M. Getmansky , D. Gray A.W. Lo, R.C. Merton, L. Pelizzon - PowerPoint PPT PresentationTRANSCRIPT
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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.
Funded by the European Union7th Framework Programme (FP7)
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
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Methodology
• We propose to measure and analyze interactions between financial institutions, sovereigns using:
– Contingent claims analysis (CCA)
– Network approach
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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
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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 inter-relationships 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
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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
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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)
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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)
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Slide 15
Mar 12
Blue InsuranceBlack SovereignRed Bank
Blue InsuranceBlack SovereignRed Bank
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Slide 16
Mar 12
Blue InsuranceBlack SovereignRed Bank
Blue InsuranceBlack SovereignRed Bank
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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)
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Slide 18
Network Measures: FROM and TO Sovereign
17 X 102= 1734 potential connections FROM (idem for TO)
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Slide 19
From GIIPS minus TO GIIPS
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June 07
Blue InsuranceBlack SovereignRed Bank
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March 08
Blue InsuranceBlack SovereignRed Bank
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August 08
GreeceBlue InsuranceBlack SovereignRed Bank
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SpainBlue InsuranceBlack SovereignRed Bank
December 11
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March 12US
Blue InsuranceBlack SovereignRed Bank
IT
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March 12
Blue InsuranceBlack SovereignRed Bank
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Early Warning SignalsJa
n01_
Dec0
3M
ar01
_Feb
04M
ay01
_Apr
04Ju
l01_
Jun0
4Se
p01_
Aug0
4No
v01_
Oct
04Ja
n02_
Dec0
4M
ar02
_Feb
05M
ay02
_Apr
05Ju
l02_
Jun0
5Se
p02_
Aug0
5No
v02_
Oct
05Ja
n03_
Dec0
5M
ar03
_Feb
06M
ay03
_Apr
06Ju
l03_
Jun0
6Se
p03_
Aug0
6No
v03_
Oct
06Ja
n04_
Dec0
6M
ar04
_Feb
07M
ay04
_Apr
07Ju
l04_
Jun0
7Se
p04_
Aug0
7No
v04_
Oct
07Ja
n05_
Dec0
7M
ar05
_Feb
08M
ay05
_Apr
08Ju
l05_
Jun0
8Se
p05_
Aug0
8No
v05_
Oct
08Ja
n06_
Dec0
8M
ar06
_Feb
09M
ay06
_Apr
09Ju
l06_
Jun0
9Se
p06_
Aug0
9No
v06_
Oct
09Ja
n07_
Dec0
9M
ar07
_Feb
10M
ay07
_Apr
10Ju
l07_
Jun1
0Se
p07_
Aug1
0No
v07_
Oct
10Ja
n08_
Dec1
0M
ar08
_Feb
11M
ay08
_Apr
11Ju
l08_
Jun1
1Se
p08_
Aug1
1No
v08_
Oct
11Ja
n09_
Dec1
1M
ar09
_Feb
12
0
1000000
2000000
3000000
4000000
5000000
6000000
7000000
8000000
9000000
10000000
0
2000
4000
6000
8000
10000
12000
14000
EL # of lines
forecast
forecast
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t=March 2008 t+1=March 2009; t = Jul 2011; t+1= Feb 2012Cumulated Exp. Loss ≡ Expected Loss of institution i + Expected losses of institutions caused by i
Early Warning Signals
Cumulative lossesMarch 09 February 12
Coeff t-stat R-square Coeff t-stat R-square# of in line# of out lines 0.40 2.92 0.23 2.2# of lines 0.87 3.5Closeness Centrality -0.63 -2.51 -0.15 -7.0
Eigenvector Centrality -0.15 -4.4 0.17 0.42
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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 11Spain
Blue InsuranceBlack SovereignRed Bank
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Slide 32
Spain
Dec 11 : EL-KMV
Blue InsuranceBlack SovereignRed Bank
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Slide 33
Blue InsuranceBlack SovereignRed Bank
CDS:Mar 12
IT
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Mar 12:EL-KMV
US
Blue InsuranceBlack SovereignRed Bank
IT
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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!