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
- 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 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 Methodology • We propose to measure and analyze interactions between financial institutions, sovereigns using: – Contingent claims analysis (CCA) – Network approach
- 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 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. Background: Feedback Loops of Risk from Explicit and Implicit Guarantees Source: IMF GFSR 2010, October Dale Gray 6
- 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
- 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. 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
- 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. 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. 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 Mar 12 Blue Insurance Black Sovereign Red Bank Blue Insurance Black Sovereign Red Bank
- 16. 16 Mar 12 Blue Insurance Black Sovereign Red Bank Blue Insurance Black Sovereign Red Bank
- 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 Network Measures: FROM and TO Sovereign 17 X 102= 1734 potential connections FROM (idem for TO)
- 23. 23 Spain Blue Insurance Black Sovereign Red Bank December 11
- 24. March 12US Blue Insurance Black Sovereign Red Bank IT
- 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. CDS data 28
- 33. 33 Blue Insurance Black Sovereign Red Bank CDS:Mar 12 IT
- 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 Thank You!
- 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. 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.