1. 1 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. 2 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. 3 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. 4 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. 5 5 Part I: Accounting-based network of sector-level exposures

6. 6 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. 7 7 Some illustrations of the EEA data Breakdown of financial instrument holdings by sector, % Evolution of sector-level net financial wealth

8. 8 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 a i,k and l j,k to estimate the individual elements X i,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. 9 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. 10 Propagation of shocks in the network Transmission of a P&L shock to sector A under mark-to-market accounting

11. 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. 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. 13 Part II: Risk-based balance sheets and transmission of risk

14. 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. 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 A i = B i - P i + J i A i = market value of assets B i = book value of debt (distress point) P i = expected loss on debt (put option) J i = junior claim (equity, call option) The model c aptures 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. 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. 17 Output: Distance to distress

18. 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. 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. 20 Thank you

21. 21 Background 1: Output: Market leverage

22. 22 Background 2: Output: Asset volatility

23. 23 Background 3: Use of networks for broader financial stability analysis How the dislocation of a bank’s balance sheet can spread Bank A i) interbank market ii) sectorsiii) countries Firm-level data Macro- financial

24. 24 Background 4: The structure of integrated accounts

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