Regulationetrisque systémique

SYstemic Risk TOmography:Signals, Measurements, Transmission Channels, and Policy Interventions

M. Billio, Ca’ Foscari University of Venice (ITALY)M. Getmansky, Isenberg School of Management, University of Massachusetts (USA)D. Gray, International Monetary Fund (IMF)A.W. Lo, MIT Sloan School of Management (USA)R.C. Merton, MIT Sloan School of Management (USA)L. Pelizzon, Ca' Foscari University of Venice (Italy) and Goethe University Frankfurt (Germany)

University of Orléans – Paris. November 5, 2013.

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, Inquire Europe, and Seventh Framework Programme FP7/2007-2013 under grant agreement SYRTO-SSH-2012-320270.

Funded by the European Union7th Framework Programme (FP7)

SYRTO

1

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

2

Methodology

• We propose to measure and analyze interactions between banks, insurers, and 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 financial 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

5

Background: Feedback Loops of Risk from Explicit and Implicit Guarantees

Source: IMF GFSR 2010, October Dale Gray 6

Background

• The size, interconnectedness, and complexity of individual financial institutions and their inter-relationships with sovereign risk create vulnerabilities to systemic risk

• We use Expected Loss Ratios (based on CCA) and network measures to analyze financial system interactions and systemic risk

7

Core Concept of CCA: Merton Model

• Expected Loss Ratio (ELR) = Cost of Guar/RF Debt = PUT/B exp[-rT]

• Fair Value CDS Spread = -log (1 – ELR)/ T

8

Moody’s KMV CreditEdge for Banks and Insurers

• 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 (RNDP) using the market price of risk, then using the sector loss given default (LGD) it calculates the Expected Loss Ratio (ELR) for banks and Insurers:

EL Ratio = RNDP*LGD=PUT/B exp[-rT]

9

Sovereign Expected Loss Ratio

• 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.

11

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 Insurers (12EMU, 6EU, 16US, 2CH, 5CA)

• CCA - Moody’s KMV CreditEdge:– Expected Loss Ratios (ELR)

Mar 12

Blue InsuranceBlack SovereignRed Bank

Blue InsuranceBlack SovereignRed Bank 13

Mar 12

Blue InsuranceBlack SovereignRed Bank

Blue InsuranceBlack SovereignRed Bank 14

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)

16

From GIIPS minus TO GIIPS

17

June 07

Blue InsuranceBlack SovereignRed Bank

18

March 08

Blue InsuranceBlack SovereignRed Bank

19

August 08

GreeceBlue InsuranceBlack SovereignRed Bank

20

SpainBlue InsuranceBlack SovereignRed Bank

December 11

21

March 12US

Blue InsuranceBlack SovereignRed Bank

IT

March 12

Blue InsuranceBlack SovereignRed Bank

23

Causal Connections

TO

FRO

MBAN SOV-NON-

GIIPS SOV-GIIPS INS

Jul04-Jun07BAN 5.54% 0.69% 1.03% 2.13%

SOV-NG 6.72% 10.00% 8.00% 5.71%SOV-G 2.07% 4.00% 20.00% 3.33%

INS 7.76% 6.90% 4.76% 5.05%Sep05-Aug08

BAN 19.86% 10.70% 4.56% 19.67%SOV-NG 20.00% 50.00% 28.00% 37.14%SOV-G 30.18% 52.00% 55.00% 43.33%

INS 8.27% 6.43% 0.48% 14.92%Jan09-Dec11

BAN 15.91% 5.06% 1.79% 11.65%SOV-NG 29.91% 8.33% 3.33% 23.33%SOV-G 32.50% 23.33% 5.00% 14.00%

INS 14.15% 3.96% 0.00% 11.79%Apr09-Mar12

BAN 13.93% 3.27% 8.93% 7.46%SOV-NG 11.31% 6.82% 8.33% 9.58%SOV-G 25.00% 13.33% 0.00% 21.50%

INS 11.79% 1.04% 2.50% 7.88%

24

EU Banks Affecting EU Insurers

25

EU Insurers Affecting EU Banks

26

EU Insurers Affecting US Banks

27

US Insurers Affecting US Banks

28

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

Apr0

1_M

ar04

Jul0

1_Ju

n04

Oct

01_S

ep04

Jan0

2_De

c04

Apr0

2_M

ar05

Jul0

2_Ju

n05

Oct

02_S

ep05

Jan0

3_De

c05

Apr0

3_M

ar06

Jul0

3_Ju

n06

Oct

03_S

ep06

Jan0

4_De

c06

Apr0

4_M

ar07

Jul0

4_Ju

n07

Oct

04_S

ep07

Jan0

5_De

c07

Apr0

5_M

ar08

Jul0

5_Ju

n08

Oct

05_S

ep08

Jan0

6_De

c08

Apr0

6_M

ar09

Jul0

6_Ju

n09

Oct

06_S

ep09

Jan0

7_De

c09

Apr0

7_M

ar10

Jul0

7_Ju

n10

Oct

07_S

ep10

Jan0

8_De

c10

Apr0

8_M

ar11

Jul0

8_Ju

n11

Oct

08_S

ep11

Jan0

9_De

c11

Apr0

9_M

ar12

EL # of lines

forecast

forecast

29

t=March 2008; t+1=March 2009; t = Jul 2011; t+1= Feb 2012Cumulated Exp. Loss Ratio ≡ Expected Loss Ratio of institution i + Expected Loss Ratios of institutions caused by i

Early Warning Signals

Cumulative Expected LossRatios

March 09 February 12Coeff t-stat Coeff t-stat

# of out lines 0.42 2.92Closeness Centrality -0.63 -2.51 -0.96 -6.40R-Square 0.17 0.24

30

Conclusion

• The system of banks, insurance companies, and countries in our sample is highly dynamically connected

• We show how one sovereign/financial institution is spreading risk to another sovereign/financial institution

• Network measures allow for early warnings and assessment of the system complexity

31

Implications

• The decision to bail out a bank or sovereign affects not only the sovereign and its own banks but also other sovereigns and foreign banks in a significant way

• Stress tests are not adequate. Need to account for interconnectedness and non-linearity in exposures

32

Thank You!

33

This project has received funding from the European Union’s Seventh Framework Programme for research, technological

development and demonstration under grant agreement n° 320270

www.syrtoproject.eu