1 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/ under grant agreement SYRTO-SSH Funded by the European Union 7th Framework Programme (FP7) SYRTO

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

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*LGD Sector It calculates the Fair Value CDS Spread Fair Value CDS Spread = -1/T ln (1 – EL Ratio)

Why EL Values? 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? 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 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 ELR k (t) = a k + b k ELR k (t-1) + b jk ELR j (t-1) + Ɛ t ELR j (t) = a j + b j ELR j (t-1) + b kj ELR k (t-1) + ζ t If b jk is significantly > 0, then j influences k If b kj is significantly > 0, then k influences j If both are significantly > 0, then there is feedback, mutual influence, between j and k. 13

Data 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)

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 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 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 12 US Blue Insurance Black Sovereign Red Bank IT

25 March 12 Blue Insurance Black Sovereign Red Bank

Early Warning Signals 26

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 Coefft-statR-squareCoefft-statR-square # of in line # of out lines # of lines Closeness Centrality Eigenvector Centrality

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 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!