Sovereign, Bank, and
Insurance Credit
Spreads: Connectedness
and System Networks
SYstemic Risk TOmography:
Signals, Measur...
2
Objectives
• The risks of the banking and insurance systems have 
become increasingly interconnected with sovereign 
ris...
3
Methodology
• We propose to measure and analyze 
interactions between financial 
institutions, sovereigns using:
– Conti...
4
Background
• Existing methods of measuring financial stability 
have been heavily criticized by Cihak (2007) and 
Segovi...
5
Background
• Most policy efforts have not focused in a 
comprehensive way on: 
– Assessing network externalities 
– Inte...
Background: Feedback Loops of Risk 
from Explicit and Implicit Guarantees
Source: IMF GFSR 2010, October Dale Gray
6
7
Background
• The size, interconnectedness, and complexity of 
individual financial institutions and their inter‐
relatio...
Core Concept of CCA: 
Merton Model 
• Expected Loss Ratio = Cost of Guar/RF Debt
= PUT/B exp[‐rT]
= ELR 
• Fair Value CDS ...
9
Moody’s KMV CreditEdge for Banks and 
Insurance Companies
• MKMV uses equity and equity volatility and default barrier (...
Why EL Values?
• EL Values are used because they do not have the 
distortions which affect observed CDS Spreads
• For bank...
Why EL Values?
• In other cases, e.g. in the Euro area periphery countries, 
bank and insurance company CDS appear to be a...
Sovereign Expected Loss Ratio
• CCA has been applied to sovereigns, both emerging market and 
developed sovereigns
• Sover...
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...
Data
• Sample: Jan 01‐Mar12
• Monthly frequency
• Entities:
– 17 Sovereigns (10 EMU, 4 EU, CH, US, JA)
– 63 Banks (34EMU, ...
15
Mar 12
Blue Insurance
Black Sovereign
Red Bank
Blue Insurance
Black Sovereign
Red Bank
16
Mar 12
Blue Insurance
Black Sovereign
Red Bank
Blue Insurance
Black Sovereign
Red Bank
Network Measures
• Degrees
• Connectivity
• Centrality
•Indegree (IN): number of incoming connections
•Outdegree (FROM): n...
18
Network Measures: 
FROM and TO Sovereign
17 X 102= 1734 potential connections FROM (idem for TO)
19
From GIIPS minus TO GIIPS
20
June 07
Blue Insurance
Black Sovereign
Red Bank
21
March 08
Blue Insurance
Black Sovereign
Red Bank
22
August 08
Greece
Blue Insurance
Black Sovereign
Red Bank
23
Spain
Blue Insurance
Black Sovereign
Red Bank
December 11
March 12US
Blue Insurance
Black Sovereign
Red Bank
IT
25
March 12
Blue Insurance
Black Sovereign
Red Bank
Early Warning Signals
0
2000
4000
6000
8000
10000
12000
14000
0
1000000
2000000
3000000
4000000
5000000
6000000
7000000
80...
t=March 2008 t+1=March 2009; t = Jul 2011; t+1= Feb 2012
Cumulated Exp. Loss   ≡   Expected Loss of institution i + Expect...
CDS data
28
29
Comparison CDS‐KMV
30
Comparison CDS‐KMV
31
CDS: Dec 11
Spain
Blue Insurance
Black Sovereign
Red Bank
32
Spain
Dec 11 : EL‐KMV
Blue Insurance
Black Sovereign
Red Bank
33
Blue Insurance
Black Sovereign
Red Bank
CDS:Mar 12
IT
Mar 12:EL‐KMV
US
Blue Insurance
Black Sovereign
Red Bank
IT
35
Conclusion
• The system of banks, insurance companies, 
and countries in our sample is highly 
dynamically connected
• ...
36
Thank You!
37
Assets  =         Equity      +        Risky Debt 
=         Equity      +        Default‐Free Debt – Expected Loss  Va...
This project is funded by
the European Union under the
7th Framework Programme (FP7-SSH/2007-2013)
Grant Agreement n°32027...
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Sovereign, Bank, and Insurance Credit Spreads: Connectedness and System Networks - Monica Billio - June 25 2013

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Sovereign, Bank, and Insurance Credit Spreads: Connectedness and System Networks - Monica Billio - June 25 2013 - First International Conference on Syrto Project

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Sovereign, Bank, and Insurance Credit Spreads: Connectedness and System Networks - Monica Billio - June 25 2013

  1. 1. Sovereign, Bank, and Insurance Credit Spreads: Connectedness and System Networks SYstemic Risk TOmography: Signals, Measurements, Transmission Channels, and Policy Interventions Monica Billio (Ca’ Foscari University of Venice), Mila Getmansky (University of Massachusetts), Dale Gray (IMF), Andrew W. Lo (MIT & AlphaSimplex Group, Cambridge), Robert C. Merton (MIT) and Loriana Pelizzon (Ca’ Foscari University of Venice) Brescia, 25 June 2013
  2. 2. 2 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
  3. 3. 3 Methodology • We propose to measure and analyze  interactions between financial  institutions, sovereigns using: – Contingent claims analysis (CCA)  – Network approach
  4. 4. 4 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
  5. 5. 5 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
  6. 6. Background: Feedback Loops of Risk  from Explicit and Implicit Guarantees Source: IMF GFSR 2010, October Dale Gray 6
  7. 7. 7 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
  8. 8. 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 8
  9. 9. 9 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) 
  10. 10. 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
  11. 11. 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
  12. 12. 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)
  13. 13. 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. 13
  14. 14. Data • Sample: Jan 01‐Mar12 • Monthly frequency • Entities: – 17 Sovereigns (10 EMU, 4 EU, CH, US, JA) – 63 Banks (34EMU, 11EU, 2CH, 12US, 4JA) – 39 Insurance Companies (9EMU, 6EU, 16US,  2CH, 5CA) • CCA ‐ Moody’s KMV CreditEdge: – Expected Loss (EL)
  15. 15. 15 Mar 12 Blue Insurance Black Sovereign Red Bank Blue Insurance Black Sovereign Red Bank
  16. 16. 16 Mar 12 Blue Insurance Black Sovereign Red Bank Blue Insurance Black Sovereign Red Bank
  17. 17. 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)
  18. 18. 18 Network Measures:  FROM and TO Sovereign 17 X 102= 1734 potential connections FROM (idem for TO)
  19. 19. 19 From GIIPS minus TO GIIPS
  20. 20. 20 June 07 Blue Insurance Black Sovereign Red Bank
  21. 21. 21 March 08 Blue Insurance Black Sovereign Red Bank
  22. 22. 22 August 08 Greece Blue Insurance Black Sovereign Red Bank
  23. 23. 23 Spain Blue Insurance Black Sovereign Red Bank December 11
  24. 24. March 12US Blue Insurance Black Sovereign Red Bank IT
  25. 25. 25 March 12 Blue Insurance Black Sovereign Red Bank
  26. 26. 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 26
  27. 27. 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 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 0.40 2.92 0.23 2.2 # of lines 0.87 3.5 Closeness Centrality ‐0.63 ‐2.51 ‐0.15 ‐7.0 Eigenvector  Centrality ‐0.15 ‐4.4 0.17 0.42 27
  28. 28. CDS data 28
  29. 29. 29 Comparison CDS‐KMV
  30. 30. 30 Comparison CDS‐KMV
  31. 31. 31 CDS: Dec 11 Spain Blue Insurance Black Sovereign Red Bank
  32. 32. 32 Spain Dec 11 : EL‐KMV Blue Insurance Black Sovereign Red Bank
  33. 33. 33 Blue Insurance Black Sovereign Red Bank CDS:Mar 12 IT
  34. 34. Mar 12:EL‐KMV US Blue Insurance Black Sovereign Red Bank IT
  35. 35. 35 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
  36. 36. 36 Thank You!
  37. 37. 37 Assets  =         Equity      +        Risky Debt  =         Equity      +        Default‐Free Debt – Expected Loss  Value Assets Equity or Jr Claims Risky Debt • Value of liabilities     derived from value of  assets. • Liabilities have  different seniority. • Randomness in  asset value.  Core Concept of CCA:  Merton Model 
  38. 38. This project is funded by the European Union under the 7th Framework Programme (FP7-SSH/2007-2013) 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.

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