Financial System Stress Tests

1. Seminar on Financial Stability and Development Financial System Stress Tests

2. Overview • Stress tests in FSAPs • Stress tests in central banks & banking supervisory agencies • Selected methodological issues • Recent developments in stress test methodology

3. Stress Tests in FSAPs Part I

4. Defining Stress Tests • Stress tests = a set of statistical techniques to help assess the vulnerability of financial institutions & financial systems to exceptional but plausible events • Defining exceptional but plausible • Against a specific historical scenario • Hypothetical scenario based on the analysis of past volatility and correlations • Other methods

5. By aggregation Individual exposures Individual institutions System-wide on bank by bank* data(“bottom up”) on aggregate data(“top down”) By methodology Sensitivity analysis Scenario analysis Contagion analysis Types of Stress Tests * Most system-wide stress tests so far have been on banks. Thus, focus on banks and the banking sector here. Nonetheless, the issue of Stress tests for nonbank financial institutions will also be discussed.

6. Recent Experience with Stress Tests • Financial crisis of late 1990s • More focus on financial sector risks • Balance sheet approach • Increasing interest in stress tests as a result • Financial institutions (BIS surveys in 2000 & 2004) • Supervisory agencies & central banks • International institutions: IMF, World Bank

7. Stress Tests in the FSAP • Stress tests a crucial tool in the quantitative assessments in FSAPs • Stress tests tailored to country-specific circumstances (complexity of the financial system, data availability)

8. Stress Tests as a Multi-Stage Process • Identify macroeconomic & market risks • Identify major exposures • Define coverage • Identify data required • Calibrate shocks (or scenarios) • Select & implement methodology • Interpret results

9. Stress Tests vs. Other Analytical Tools • Advantages of stress tests • Forward-looking assessment of risks • More precise than analyzing aggregated indicators (NPLs, open positions) • Disadvantages of stress tests • Robustness with respect to assumptions? • Banks viewed as static portfolios rather than as dynamic units • Role of non-quantitative factors?

10. Other Analytical Methods Complement Stress Tests • Macroprudential analysis • Financial Soundness Indicators (FSIs) • Links between FSIs and other factors, especially the macroeconomic framework • Analysis of qualitative information • Legal, regulatory, accounting, tax conditions • Corporate governance

11. Evolving Role of Stress Tests in FSAP • Most missions: single-factor sensitivity analysis based on historical extremes • More recent FSAP missions • More focus on scenario analysis • Active involvement of the authorities • Industrial countries: bank internal stress test models, authorities’ macroeconomic models, inclusion of interbank contagion risk • Inclusion of nonbank financial institutions in some cases

12. Stress Tests in Central Banks & Banking Supervisory Agencies Part II

13. Role of Stress Tests • Help focus supervisory processes on a risk basis • Support macroprudential analysis at the central banks (including in financial stability reports) • Assess effects of prospective policy changes

14. Stress Tests Conducted by Central Banks & Banking Supervisors • Bank-by-bank stress tests conducted by supervisors • Financial system stability reports include or refer to aggregate stress tests • Range of approaches – examples: • Hungary (focus on sensitivity calculations) • Norway (focus on sources of credit risk)

15. Implementation Issues • Coverage – institutions • Coverage – exposures • Methodology • Organization

16. Coverage - Institutions • From systemic stability perspective, important to cover a significant part of the system, but not all institutions • But for supervisory purposes, it might be useful to increase the coverage • Coverage of foreign banks (branches, subsidiaries, exposures to foreign banks) • Consolidation (consistency across time, inclusion of non-bank financial institutions)

17. Coverage - Exposures • Basic exposures to be included • Credit risk (incl. indirect FX & interest rate risks) • Direct interest rate risk • Direct FX risk • Other exposures to be considered • Interbank contagion risk • Equity and/or real estate price risk • Later: liquidity, concentration risks • Include on- & off-balance sheet exposures

18. Methodology • Both sensitivity and scenario analysis • Scenario analysis: a combination of historical and hypothetical scenarios • For the hypothetical scenarios, justification using macroeconomic models (see later) • A mix of top down & bottom up • Factors: computation difficulty, confidentiality • Most calculations typically bank-by-bank, but model estimates on aggregate basis

19. Methodology • Expressing the impact: in terms of capital adequacy (pre-shock vs. post-shock); in terms of liquidity for liquidity stress test • Impact measured over what period? • Typically one year (ideally, NPV of future impact) • Treatment of profits • A prudent assumption based on the past • An autonomous shock to non-interest income or net interest income (on top of other shocks)

20. Organization • How frequently? • Standard set of tests on quarterly basis, market risk/sensitivity analysis more frequently, elaborate analysis (e.g. contagion) less frequently • Run by whom? • Which software? • Many start with Excel and E-Views, then integrate with supervisory information systems • Presentation, dissemination of the results • By bank (supervision; links with EWS) • By peer groups (macroprudential surveillance)

21. Selected Methodological Issues Part III

22. Selected Methodological Issues • Selecting macroeconomic scenarios • Foreign exchange (FX) risk • Interest rate risk • Credit risk • Interbank contagion risk • Liquidity risk • Equity price & real estate price risk

23. Macro Scenarios for Stress Tests • Historical scenarios • e.g. the 1997 turbulence and subsequent slowdown in East Asia • Hypothetical scenarios • Recognizing the limitations of macro models, especially for large shocks, would it be possible to use the central bank’s existing macro model? • Stochastic simulations based on the model? • Scenario design: relative sizes of shocks to the risk factors • Assessing likelihood of the scenarios

24. Worst Case vs. Threshold Approach

25. Controversy on Probability • Stress test scenarios should not be attached any probability measure, ST is different from the standard risk-modeling toolkit (e.g., Kupiec, 2001) • Stress test scenarios need to be attached an explicit probability measure; otherwise they are useless (e.g., Berkowitz, 1999)

26. Controversy on Probability • Practical solutions • Base stress test on extreme historical scenario • Relate stress test to extreme historical scenario • Use threshold approach with extreme thresholds (useful when exposures negligible: proof by contradiction) • Combine historical scenario with hypothetical assumptions (robustness analysis)

27. Foreign Exchange Risk • The standard sensitivity analysis Note: C=capital, ARW=risk-weighted assets, F=net open position, e=exchange rate • The impact on capital adequacy roughly equals the shock times the open position…

28. Foreign Exchange Risk • ... but stress test must reflect non-linearity arising from FX options • Off-balance sheet (OBS) positions, which include options, are not negligible in many countries. Example

29. Indirect FX Risk • Nonperforming loans vs. exchange rate • Usually much more significant than the direct FX risk • The analysis requires • Regression of leverage vs. NPLs • Inclusion of stock and flow exposures in FX

30. Interest Rate Risk Duration is the key indicator, because This allows to express changes in capital adequacy ratio as where

31. Interest Rate Risk—Issues • Adequacy of the available data, including • Do banks report residual maturity properly? • Does the indicator capture the whole balance sheet? • Are off-balance sheet contracts included? • Simplified method: residual maturity plus weigths proposed by Basel Committee • Nonlinearity (duration changes with large changes in interest rates) • NPV may differ from the regulatory capital • Correlation between risk-weighted assets and assets • Indirect interest rate risk (see under credit risk)

32. Credit Risk Modeling • The most significant source of risk • Also, the most in need of strengthening • Mechanical approaches • Approaches based on corporate sector data (leverage, interest coverage) & possibly household sector data • Approaches based on loan performance data (including the VAR model already estimated)

33. 1. Mechanical Approaches • Assume an inflow of new NPLs • Function of existing NPLs, performing loans, or a weighted sum of the two • Assume ↑ provisions on existing NPLs • Increase in provisioning rates • Credit migration within NPLs (“transition matrix”) • Credit expansion model: inflow of new loans, followed by credit migration to and within NPLs • Do the above by sectors (e.g. corporate & household)

34. 2. Data on Borrowers • Leverage vs. NPLs (a possible model) * • Top-down calculations * Notes: Based on an actual model used by IMF staff for cross-country panel data estimates. Npls – ratio of non-performing loans to total loans, lev – leverage ratio, rcc – real cost of capital, reer– real effective exchange rate, y-hat– real GDP growth rate, p-hat – inflation rate, m-hat – growth rate of M1, d-hat– growth rate of domestic credit, roe – corporate sector return on equity

35. 2. Data on Borrowers • Logit model predicting individual bankruptcy probabilities as a function of age, size, industry characteristics & corporate soundness indicators (earnings, liquidity, financial strength) • Include interest and exchange rates on the right hand side (to capture the indirect risk) • Link to individual banks through their exposures to the various groups of companies • Predict bank potential losses (also taking into account collateral)

36. 2. Data on Borrowers • A simpler approach: exposure variables • Net open FX position & ratio of FX income to FX costs (for indirect FX risk) • Interest coverage (for indirect interest risk) • If exposure variable exceeds an estimated (assumed) threshold, default rate rises • Similarly to previous approach, translate to bank losses (after collateral)

37. 3. Loan Performance Data • Advantages • Also available for household sector(with rapid lending growth in many countries) • Should be more readily available than leverage • Disadvantage • Lagging indicators of asset quality

38. Introducing Contagion Risk • Need to compile data for the following matrix

39. Introducing Contagion Risk • Exposure = all uncollateralized lending (including both on- & off-balance sheet exposures) • Currently, only data on total exposure of a bank to interbank market are available • Two types of the contagion stress test • “Pure” contagion test: A “fraud” in a bank; impact on other banks through interbank exposures • “Macro” contagion test: Macro shocks are grossed-up to trigger failure of weakest bank; followed by interbank contagion

40. Introducing Contagion Risk Si’ = (Ci-E1i)/(Ai-Ei1), where i=2, 3, and 4. • Implementation (example for 4 banks) ... estimate as a part of the EWS model S2’’=(C2-E12-P3*E32-P4*E42)/(A2-E21-P3*E23-P4*E24) S3’’=(C3-E13-P2*E23-P4*E43)/(A3-E31-P2*E32-P4*E34) S4’’=(C4-E14-P2*E24-P3*E34)/(A4-E41-P2*E42-P3*E43)

41. Introducing Contagion Risk Aggregate stress test vs. interbank contagion stress test

42. Equity & Real Estate Price Risk • Equity price risk—similar to FX risk • Net open positions in equities • Need to include off-balance sheet exposures • Banks’ exposure to real estate price risk • Direct exposure (investment in real estate) • Credit exposure (developers etc.) • Degree of real estate collateralization • loan to value ratio • default probability (from credit risk stress test)

43. Concentration Risks (Credit) • Simple example: sensitivity analysis for large exposures • More sophisticated example • Run regressions for default probability on corporate data (company-by-company), with dummy variables for the sectors/regions • Ways to define default probability (actual default—run a logit regression; or set a threshold for interest coverage ratio) • For a set of a bank’s exposures to sectors/regions, calculate implied default probability

44. Liquidity Risk • Focus on bank liquidity stress tests • Results reported off-site, validate during on-site visits • Off-site cross-check (sensitivity analysis) • Overall risk: assume a % of deposits withdrawn (percentages determined based on past bank runs, vary for different maturities) • Concentration risk in deposits (same as above, but for a percentage of the largest deposits)

45. Recent Developments inStress Test Methodology Part IV

46. Bank Internal Stress Test Models • Two surveys of stress test practices in commercial banks (2000 & 2004) • More attention to bank internal stress tests in on-site visits • Consider issuing guidelines on stress tests in commercial banks? • Cross-check results of bank & supervisor stress tests

47. Stress Tests vs. Early Warning Systems • Consider designing an EWS system in the form of a statistical model of detection of bank failure/stress • Could be back tested against the ratings • BIS working paper on EWS for banking supervision

48. Cross-Market Contagion • Coverage of non-bank financial institutions in the framework for consolidated supervision • Contagion between banks and non-bank financial institutions—e.g. insurance companies • Credit derivatives

49. Further Reading • Blaschke et al., 2001, “ST of Financial Systems: An Overview of Issues, Methodologies, and FSAP Experiences,” IMF WP 01/88 • www.imf.org/external/pubs/cat/longres.cfm?sk=15166.0 • IMF & WB, 2003, “Analytical Tools of the FSAP” • www.imf.org/external/np/fsap/2003/022403a.pdf • Čihák, 2004, “ST: A Review of Key Concepts,” Czech National Bank technical note 2/2004 • http://www.cnb.cz/en/pdf/IRPN_2_2004.pdf