Balance Sheet Contagion and Systemic Risk in the Euro Area Financial System: a Network Approach

1. Balance Sheet Contagion and Systemic Risk in the Euro Area Financial System: a Network Approach Olli Castrén and Ilja Kavonius ECB Workshop “Recent Advances in Modelling Systemic Risk using Network Analysis” 5 October 2009

2. Outline of the presentation • Key concepts and literature • Part I: Accounting-based network of sector-level exposures • Data issues • Constructing the network • Simulating balance sheet contagion • Part II: Risk-based balance sheets and transmission of risk • The contingent claims approach • Calculation of sector level credit risk indicators • Contagion of risk exposures in the risk-based network • Discussion and outlook for future work

3. Key concepts • The role of balance sheet interlinkages, leverage and asset volatility as key financial vulnerabilities at the sector level • At the macro-level, contagion and shock propagation can take place via balance sheet cross-exposures, as someone’s assets are someone else’s liabilities • But accounting-based balance sheet say nothing about accumulation and transmission of risk exposures • For a richer analysis, a framework is needed to move to risk-based balance sheets

4. Some related literature Theory contributions to analysis of balance sheet linkages • Credit chains and balance sheet contagion • Kiyotaki and Moore (JPE 1997, AER 2002) • Liquidity shocks and systemic risk • Brunnermeier and Pedersen (RFS, 2009), Shin (JFI 2008) Empirical applications: • Aikman et al (BoE WP #372, 2009), plus work at BIS, IMF • Interbank contagion literature • Growing literature on financial networks Work on risk-based balance sheets • Gray, Merton and Bodie (2007), Gray and Malone (2008) Main contributions of this paper: apply sector level data to balance sheet networks and to analysis of risk contagion

5. Part I: Accounting-based network of sector-level exposures

6. Data issues • Euro area financial accounts (EAA): Holdings of various financial instruments by the various sectors,both on the asset and the liability sides • Use 8 main financial instrument categories and 7 sectors (based on the ESA95 classification) • Quarterly data for the euro area from 1999 Q1 • A closed system (using the rest of the world sector): each financial liability of a sector is an asset for some other sector • The financial accounts are linked to the real accounts via the net lending/borrowing positions (net financial wealth) • Non-financial assets (including housing) have no counterparties on the liability side and are not available on a quarterly basis; excluded from this analysis

7. Some illustrations of the EEA data Breakdown of financial instrument holdings by sector, % Evolution of sector-level net financial wealth

8. Constructing the network of exposures • The data provide instrument-specific total holdings of assets and liabilities by each sector • Can use information on the relative distribution of the sum elements ai,kand lj,k to estimate the individual elements Xi,j for each instrument category => provides the who-to-whom links • We get bilateral linkages for all 8 instrument categories • Works nicely with non-consolidated data

9. Constructing the network of exposures Cross-sector gross balance sheet exposures in the euro area financial system The key role played by the banking sector

10. Propagation of shocks in the network Transmission of a P&L shock to sector A under mark-to-market accounting

11. Propagation of shocks in the network Example: a cash-flow shock on the NFC sector that corresponds to a 20% loss in shareholder equity

12. Propagation of shocks in the network • In a multi-period framework, agents are expected to balance their accounts after the shock • In the current context, this would amount to asset dis-investment, or a de-leveraging process • Need to specify rules for: • Target level of leverage • Assets to be shed • The purchasing party • The impact on the asset price • The framework allows for simulation of such processes once the rules have been defined

13. Part II: Risk-based balance sheets and transmission of risk

14. The role of risk-based balance sheets • The accounting-based network neatly illustrates shock transmission in the system but it says nothing about risk exposures and systemic risk • Yet financial crises are typically a result of accumulated vulnerabilities in the form of risk exposures, triggered by sudden bursts of volatility • To have early warning properties, the framework should include these characteristics • A solution is to construct stochastic risk-based balance sheets which encompass the deterministic accounting-based model

15. The contingent claims approach to macro-financial risk analysis • Contingent claims analysis (CCA) measures the expected losses of balance sheet items • Idea: model debt of the sector as a put and equity as a call option, and estimate the market value of assets • The balance sheet of sector i then becomes • Ai= Bi - Pi+ Ji • Ai = market value of assets • Bi = book value of debt (distress point) • Pi = expected loss on debt (put option) • Ji= junior claim (equity, call option) • The model captures several key financial stability factors: leverage, volatility and non-linearity • By assuming that volatility is zero, the framework collapses to the accounting-based model

16. Input data • To estimate the risk-based balance sheets, we need balance sheet data on equity and other liabilities, plus market data on equity volatility, asset returns and interest rates • Using the techniques developed by Moody’s KMV, market value of assets and asset volatility are estimated at an intermediate stage, once distress points have been estimated • Equity is measured by shareholder equity plus net financial wealth.Data on equity volatility are implied volatilities of the relevant sector-level stock indices. • For the household and government sector (no equity issued), government bond yield volatility is used

17. Output: Distance to distress

18. Output: “Network” of pair-wise correlations between sector-level distances-to-distress Note: The thick link shows correlation between sector-specific distance-to-distress measures that exceeds 0.75, the intermediate link shows correlation between 0.5 and 0.75 and the thin link between 0.25 and 0.5.

19. Discussion and future work • Network models applied to the macro level provide new information about sector-level linkages and shock transmission channels • Can detect important risks and vulnerabilities that might go undiscovered in sector-specific analysis • Including risk exposures shows how correlations and contagion risk change over time • Complements the outputs from other models, including those using sector and firm-level information • More work is needed to refine the propagation mechanisms and the CCA balance sheets

20. Thank you

21. Background 1: Output: Market leverage

22. Background 2: Output: Asset volatility

23. Background 3: Use of networks for broader financial stability analysis How the dislocation of a bank’s balance sheet can spread ii) sectors iii) countries Macro-financial i) interbankmarket Bank A Firm-leveldata

24. Background 4: The structure of integrated accounts

25. Background 5: The CCA model in brief Key drivers of distress risk: leverage, volatility and asset return