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

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

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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

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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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)

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


16

Mar 12



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

20

June 07


21

March 08


22

August 08

GreeceBlue InsuranceBlack SovereignRed Bank

23

SpainBlue InsuranceBlack SovereignRed Bank

December 11

March 12US


IT

25

March 12


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

29

Comparison CDS‐KMV

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Comparison CDS‐KMV

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CDS: Dec 11Spain


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Spain

Dec 11 : EL‐KMV


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CDS:Mar 12

IT

Mar 12:EL‐KMV

US


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

36

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.

http://www.syrtoproject.eu

Economy & Finance

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