sovereign, bank, and insurance credit spreads: connectedness and system networks - monica billio -...
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Sovereign, Bank, and Insurance Credit Spreads: Connectedness and System Networks - Monica Billio - June 25 2013 - First International Conference on Syrto ProjectTRANSCRIPT
Sovereign, Bank, and Insurance CreditSpreads: 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
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
Methodology
• We propose to measure and analyze interactions between financial institutions, sovereigns using:
– Contingent claims analysis (CCA)
– Network approach
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
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
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‐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
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
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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)
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) + ƐtELRj(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)– 63 Banks (34EMU, 11EU, 2CH, 12US, 4JA)– 39 Insurance Companies (9EMU, 6EU, 16US, 2CH, 5CA)
• CCA ‐Moody’s KMV CreditEdge:– Expected Loss (EL)
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Mar 12
Blue InsuranceBlack SovereignRed Bank
Blue InsuranceBlack SovereignRed Bank
16
Mar 12
Blue InsuranceBlack SovereignRed Bank
Blue InsuranceBlack SovereignRed Bank
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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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 InsuranceBlack SovereignRed Bank
21
March 08
Blue InsuranceBlack SovereignRed Bank
22
August 08
GreeceBlue InsuranceBlack SovereignRed Bank
23
SpainBlue InsuranceBlack SovereignRed Bank
December 11
March 12US
Blue InsuranceBlack SovereignRed Bank
IT
25
March 12
Blue InsuranceBlack SovereignRed Bank
Early Warning Signals
0
2000
4000
6000
8000
10000
12000
14000
0
1000000
2000000
3000000
4000000
5000000
6000000
7000000
8000000
9000000
10000000
Jan0
1_De
c03
Apr01_Mar04
Jul01_Jun0
4
Oct01
_Sep
04
Jan0
2_De
c04
Apr02_Mar05
Jul02_Jun0
5
Oct02
_Sep
05
Jan0
3_De
c05
Apr03_Mar06
Jul03_Jun0
6
Oct03
_Sep
06
Jan0
4_De
c06
Apr04_Mar07
Jul04_Jun0
7
Oct04
_Sep
07
Jan0
5_De
c07
Apr05_Mar08
Jul05_Jun0
8
Oct05
_Sep
08
Jan0
6_De
c08
Apr06_Mar09
Jul06_Jun0
9
Oct06
_Sep
09
Jan0
7_De
c09
Apr07_Mar10
Jul07_Jun1
0
Oct07
_Sep
10
Jan0
8_De
c10
Apr08_Mar11
Jul08_Jun1
1
Oct08
_Sep
11
Jan0
9_De
c11
Apr09_Mar12
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 ‐7.0Eigenvector Centrality ‐0.15 ‐4.4
0.17 0.42
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CDS data
28
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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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Spain
Dec 11 : EL‐KMV
Blue InsuranceBlack SovereignRed Bank
33
Blue InsuranceBlack SovereignRed Bank
CDS:Mar 12
IT
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!
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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
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.