Presentation Transcript

1. Developing Macro-stress tests: how to identify systemic risk

   Session 10 Mindaugas Leika
2. Macroprudential policy framework I. Macroprudential policy definition, targets, policy transmission channels and relationships with other policies (Monday) II. Institutional structure (Tuesday) III. Policy tools (Tuesday) IV. Risk identification and quantification: stress testing (This lecture)
3. AGENDA How to identify systemic risk: role of stress testing Advances in systemic risk research and monitoring
4. Where is stress testing? (i)
5. Where is stress testing? (iI) Macro Stress testing
6. Description vs. Prescription Source: Christensen C., Carlile P. (2009)
7. Stress testing challenges Data availability Data complexity Data relevancy Model uncertainty Scenario uncertainty
8. What is systemic risk? Imbalances Correlated exposures Excessive exposures Spillovers to the real economy Disruption of financial transactions, flow of funds Capital flight Bubbles, contagion Bank closures Various studies offer different measures of risk. However, what is relevant in one country might not be relevant in another one, and this is especially important in Africa: various advanced tools might be too premature to be employed. Many studies provide a good description of what happened in the past, but can they predict the future?
9. Identification: metrics A. Lo at all (2012) provides an extensive list of various systemic risk measures and data requirements: Macroeconomic: asset price bubbles, credit, investment cycles; macroprudential regulation Granular and Network measures: default intensity, network analysis and systemic financial linkages; scenario analysis; risk and shock transmission; mark-to-market accounting and liquidity pricing; Forward looking risk measures: Contingent claim analysis, multivariate density estimation, housing sector simulations, principal component analysis etc. Stress test measures: macro stress tests Cross-Sectional Measures: CoVaR, expected shortfall etc. Illiquidity and insolvency: risk topography, leverage cycle, crowded trades, hedge fund trades etc.
10. Identification: data (1) Asset price boom/bust cycles: National statistics and/or international data sources: IMF International Financial Statistics Macroeconomic indicators: national statistics office, national accounts data (e.g. financial accounts) Bank funding risk and shock transmission: BIS locational banking statistics; Bank surveys (on financing terms); deposit statistics (interest rates); Refinancing schedule (timing of bonds to be redeemed, loans repaid etc.); Data on covenants (e.g. repayment obligation link with credit rating).
11. Identification: data (2) Corporate and consumer indebtedness: - Banks’ proprietary data; credit register (both positive and negative ones); - National accounts statistics: corporate and household leverage ratios; Contingent claim analysis: - Moody’s KMV, Bloomberg, MarkIt; CDS spreads; CoVaR, currency trades, insurance premiums, etc. - Market data, various service providers: Bollomberg, Reuters Datastream, Moody’s, S&Ps etc. However, in many cases this is not very relevant for African countries (dominated by plain vanilla banks), as many instruments are not available in local markets.
12. Recommendations Avoid “Sophistication trap”, i.e. the more sophisticated analysis is, the more credibility it has. This is wrong approach, especially in countries where simple financial systems dominate. Avoid sophisticated models, if data is not reliable and even not available. Concentrate on data availability and timing issues: simple ratios can tell much more, than distance to default or first principal component. Try to build simple models and for systemic risk, create network of individual exposures, observe common patterns.
13. Example: from individual bank run towards systemic crisis
14. Individual bank run (1) As soon as even a small fraction of deposits is channeled towards illiquid assets (long-term loans, investments etc.) every bank won’t be able to meet its obligations towards creditors (depositors), hence there is always a risk of liquidity crisis. Imagine, that we have a three period game: deposit D is placed at t=0 and can be withdrawn at t=1 (impatient depositors) and t=2 (patient depositors). Bank invest deposits into long term assets that yield rate of return R. R>1 at time period t=2, however R<1 at time period t=1. Hence, if bank needs to liquidate investment before it matures, it makes a loss (R<1) (e.g. fire-sale of assets). If bank knows the proportion of depositors  who are impatient and withdraw at time t=1, it invests D share of deposits into liquid assets and (1-)D share of deposits into less liquid (long-term) assets.
15. Individual bank run (2) This system is called a Fractional Reserve Banking system. In practice, banks need to make their own liquidity forecasts to obtain , or stick to minimum reserve and liquidity requirements imposed by regulators. This system works well, if  is stable, however is  is higher than anticipated, the bank will have to liquidate higher proportion of assets under depressed prices and suffer loss. If this happens, patient depositors realize, that they won’t be able to get back their deposits at time t=2 in full amount. Bank run in this case is a rational strategy; depositors’ expectations are self-fulfilling. Of course, there is a case when R is much higher than 1, so higher  might not necessarily lead to bank liquidation.
16. Individual bank run (3) These assumptions were incorporated in the classical Diamond-Dybvig framework. If depositors type is observed, it is possible to prohibit early withdrawal (contract design or by law); otherwise banks need to rely on historical observations. However a lack of confidence in a given bank would lead to a coordination failure among depositors. Hence, the bank needs to obtain liquidity in the market and/or from a central bank or liquidate investments. If interbank market is functional and liquidity needs are bank specific rather than system, bank might be able to obtain liquidity by borrowing. It is also possible, that the bank simply securitizes its long-term assets (the case of Northern Rock). Due to inter-linkages among banks, other financial institutions, payment and settlement systems etc. individual bank run might lead to the general loss of confidence in the banking system and as a result – systemic crisis. Alternative solutions: suspension of convertibility (also “bank holidays”), deposit insurance, debt-equity swap.
17. Systemic crisis (1) Due to inter-linkages among banks, other financial institutions, payment and settlement systems etc. individual bank run might lead to the general loss of confidence in the banking system and as a result – systemic crisis. Systemic crisis can be triggered via system of cross-holdings among banks. Market topology is important: a system where each bank borrows from one or several banks is more fragile than a more diversified interbank system.
18. Systemic crisis (2) Bank A Bank A Bank E Bank D Bank B Bank B Bank C Bank C Incomplete structure: more risky to contagion effects Complete structure: less risky to contagion effects
19. Origins of liquidity shocks Market events Cross-border contagion Loss in confidence Speculations (rumors) Interbank contagion Liquidity ratio below threshold Negative cash flow Credit losses Operational losses Change in investors expectations Rating downgrade
20. Vicious circle 1. Run on a bank 4. Assets value, market prices 2. Liquidity crisis 3. Interbank interest rates, Real interest rate 
21. Stress in the FedWire Source: Bech M. et all (2009)
22. Advances in systemic risk research and monitoring
23. From Individual risk to systemic risk Most of the studies before the GFC focused on individual or firm level risks: VaR, portfolio concentration, diversification, credit ratings etc. There were studies on joint probability of default, but not on bank, but firm level SR studies flourished during this crisis: CoVaR, measures of interconnectedness, spillover effects, risk transmission, deleveraging etc.
24. Studies (1) IMF Cross-Border contagion model. Utilizes BIS statistics and uses Input-Output type of matrices of cross-border exposures. http://www.imf.org/external/np/pp/eng/2010/090110.pdf Adrian, Brunnermeier (2009) CoVaR. Co means: conditional, contagion, comovement. It is VaR of the whole financial sector conditional on institution i being in distress. http://www.princeton.edu/~markus/research/papers/CoVaR.pdf Information cascades and Big data analytics. Abreu, Brunnermeier (2003). http://www.princeton.edu/~markus/research/papers/bubbles_crashes.pdf
25. Studies (2) Goodhart, Segoviano(2009). Banking Stability Measures. Measures distress dependence among banks. http://www.imf.org/external/pubs/ft/wp/2009/wp0904.pdf Abbe, Khandani, Lo (2011) Privacy-Preserving Methods for Sharing Financial Risk Exposures. Data collection: how to obtain aggregate number without disclosing individual exposure? http://bigdata.csail.mit.edu/node/23
26. Example: real estate linked loans (1) Source: Abbe, Khandani, Lo (2011)
27. Example: real estate linked loans (2) Source: Abbe, Khandani, Lo (2011)
28. Measures of interconnectedness: how the World became more and more interconnected Source: Billo, Getmansky, Lo, Pelizzon (2012). Measuring Systemic Risk in the Finance and Insurance Sectors