Outline

2. Outline • Importance of stress testing • Methodologies for stress testing • Impact on P&L and capital • Countercyclical provisioning • Some caveats on stress testing • Conclusions • Annex. Dynamic provisioning

3. Importance • Stress testing is an important risk management tool for banks and, in general, for financial companies • Market risk is a natural way to start (lot of information, variability of exposures and prices,…) • Increasing importance of stress testing credit risk • More data available (IRB approaches in Basel II) • Importance of credit risk • Liquidity stress testing also attracts growing interest • Already before August 2007 • Much more since August 2007

4. Importance • Stress testing is also an important risk measurement tool for bank supervisors and central banks • impact of an increase of 50% in mortgage PDs? • what if GDP falls to 0 two consecutive quarters? • Credit risk, market risk, liquidity risk, as well as contagion can be simulated • Assess the resilience of the banking system • individual firms, both in terms of P&L and solvency • whole banking system • special interest on large and/or complex banks

5. Methodologies • Top-down: • supervisors and/or central banks carry out the impact analysis of different scenarios on banks profits, solvency and resilience • Bottom-up: • with the scenarios provided by the supervisor, each bank, according to their own internal models and estimates evaluate the impact of the scenario on their own P&L and solvency ratios. Later on results are aggregated

6. Methodologies • Sensitivity analysis: • impact of the change in a variable • Increase in PDs by 2 standard deviations • Scenario analysis: • impact of the change of a set of interrelated variables • change in the macroeconomic scenario

7. Methodologies–Pre FSAP • IMF Financial Sector Assessment Programs • an stimulus for stress testing financial systems • Long before the FSAP, we started with a macro stress testing on problem loan ratios • Top-down approach • Sensitivity analysis

8. Methodologies–Pre FSAP • Long term equilibrium relationship between macro variables… • GDP growth, interest rates and NPL ratios • … and a short term error correction mechanism • unemployment, interest rates, indebtedness… • Shocks on GDP growth and interest rates • 1 and 2 standard deviations • a crisis scenario (deep recession) • Delgado and Saurina (2004), under review

9. Methodologies–Pre FSAP • Cointegration techniques for the estimation of the : • Long run relationship • Short run adjustment mechanism • 20 years of the Spanish economy 1982-2002 • Problem loans: • Non-performing • Doubtful assets (still performing but with low recovery probability) • Database: • Commercial and savings banks: accounting reports • Householders and firms: Credit Register

10. Methodologies–Pre FSAP

11. Methodologies–Pre FSAP • Variables used: • real GDP growth rate (significant and -) • 3 month interbank interest rate (significant and +) • Unemployment rate (not significant) • Indebtedness ratios (not significant): • Households: loans/gross income • Firms: liabilities/GDP • Debt burden: indebtedness * interest rate (not significant)

12. Methodologies–Pre FSAP • The model accuracy can be tested • Commercial banks:

13. Methodologies–Pre FSAP

14. Methodologies–Pre FSAP

15. Methodologies–FSAP • Thorough stress testing • Top-down • simulation and scenario analysis • macro model, impact on NPL, impact on P&L, impact on solvency ratios • importance of detailed information: Credit Register • Bottom-up • focus on large banks • rely on their own internal models • Bottom-up panel data approach • Stress testing might be simpler than expected • conceptually • practically

16. Methodologies– FSAP –Top down • Macroeconomic model (general equilibrium model) • Satellite equations (i.e. for credit growth) • Simulate different scenarios • decline in house prices • Increase in oil prices • US dollar depreciation • Problems in the two largest Latin American countries • Impact on NPL and on the P&L • Equation for NPL • Different equations for different P&L items • Impact on solvency ratios

17. Methodologies – FSAP – Bottom up • Seven banks involved (2/3 of total assets) • Assess the impact of the shocks provided by BoS/IMF on their balance sheets and P&Ls • Sensitivity analysis • market risk; interest rate risk; liquidity risk • credit risk • Scenario analysis • decline in house prices • Increase in oil prices • US dollar depreciation • Problems in the two largest Latin American countries • All the banks involved had proper tools to manage the risks analysed

18. Methodologies-Bottom up–Credit risk • Not only the seven banks but also for the whole banking system • New methodology: shocks on PDs and portfolio differentiation • Use of our Credit Register • Any loan over 6,000 euros granted by any bank operating in Spain to both, individuals and firms • Calibration: change in annual PD from 1990 to 2004, firms and mortgages • Impact on loan loss provisions and, therefore, on profits and own funds

19. Methodologies-Bottom up–scenario analysis • 4 macro scenarios • Several macro models (BoS+NiGEM+OEF) + satellite equations (for credit and NPL ratios) • At an aggregate level (by BoS) and also bottom up from the 7 banks participating in the FSAP • Assessing the impact of the scenarios on the balance sheet and the P&L of each of the 7 banks • Use of internal information of banks (management information and own internal budgets) • Impact on PD translated on loan loss provisions • We were able to carry out bottom up approaches because the banks had internal models for credit risk

20. Methodologies-Bottom-up panel data • Additional/robustness exercise combining • scenario analysis • individual accounting and solvency data • Credit Register information • Simple methodology • PD modelling bank by bank along time • expected losses, impact on P&L and solvency ratios • dispersion analysis • overall robustness but, maybe, some fragility at particular credit institutions • Complete coverage of credit risk stress testing

21. Methodologies-Bottom-up panel data • Commercial and savings banks (90% of total assets) • Period 1992-2004 • NPL ratios • Mortgages • Consumer loans • Construction and Real Estate • Rest of non-financial firms • Credit Register data • We have information on every loan above 6.000 euros • In particular, we know whether the loan is in default or not

22. Methodologies-Bottom-up panel data • Target: • To model NPL determinants as a function of macro variables in order to simulate impact of changes in the macro scenario • Panel data analysis

23. NPL ratios by business segment (%)

24. Impact of macro scenarios

25. Stress of macro scenarios

26. 90th percentile

27. Methodologies-Bottom-up panel data • 4scenarios Banco de España/IMF plus stress scenario (1% and 0% GDP growth) • Impact of macro variables on NPL and EL (through PDs) • Impact of EL on profits and capital • median and 90th percentile bank • Impact on median banks • Considering general loan loss provisions • the whole EL covered in all the scenarios • no impact on profits and own funds • Impact on 90th percentile bank • Small relative weight in terms of total assets • No systemic risk, reputation risk • Taking into account general loan loss provisions • Some impact on profits, marginal impact on own funds

28. Methodologies-FSAP • The stress testing results show the importance of a countercyclical loan loss provision • Need for countercyclical mechanism?... • … through LLP and/or capital (i.e. Pillar 2) • In the annex we develop how a countercyclical mechanism works

29. Methodologies–Post FSAP • Loss distribution for credit risk • Credit Register data • Modelling 12 sectors • 10 industries • Mortgages • Consumption loans • Close to 90 quarters • Jiménez and Mencía (2007), Working Paper 0709, Banco de España

30. Methodologies–Post FSAP • PDs and number of loans depend on GDP growth and interest rates, two latent factors uncorrelated with the business cycle and a sector idiosyncratic factor • Modelling LGDs • Three year aggregation of losses • VaR 99.9%, well below the amount of provisions and own funds • Stress testing • deep recession (i.e. GDP decline during 4 quarters and slow recovery from that) • moderate reduction in capital levels

31. Methodologies–Post FSAP

32. Some caveats on stress testing • Structural break • in the economy: • joining a monetary union, lower levels of interest rates and lower volatility • change in long-term relationships • shift in the response to shocks • in risk management by banks • improvement in measurement of credit risk • improvement in management of credit risk (securitization, credit derivatives, transfer of risk, more weight to control risk departments,…) • shift in the impact of shock on banks • Uncertainty about the degree of confidence on stress testing results

33. Some caveats on stress testing • How reliable are stress tests results? • Backward looking • Distance from the last recession • dependence on the level of the series… • but the probability of changing regime might be higher • How to react to a bad news stress testing exercise? • We are close to the shock almost no degree of freedom to react even counterproductive to react • We are far away from the shock • increase in complacency vs killing the expansion • Stress testing does not help to answer those policy dilemmas… • …but it is a useful tool for supervisors and central banks

34. Some caveats on stress testing

35. Conclusion • There is no mystery in stress testing • Methodologies are relatively simple and cheap • Data availability and its quality is probably the most binding element • Good risk management tool for banks as well as for supervisors…. • …taking into account some caveats

36. ANNEX • DYNAMIC PROVISIONING

37. Annex-dynamic provisioning • Banks’ lending mistakes are more prevalent during upturns Borrowers and lenders are overconfident about investment projects Banks’ over optimism implies lower credit policy standards • During recessions, banks suddenly turn very conservative and tighten credit standards • Increasing competition makes things worse • A monetary policy too lax for a too long period might also increase risk taking incentives by banks (search for yield) • Collateral might also play a role in credit cycles • Loan booms are intertwined with asset booms • All in all, lending cycles with impact on the real economy

38. Annex-dynamic provisioning • Jiménez and Saurina (International Journal of Central Banking, 2006) • Evidence of a direct, although lagged, relationship between credit growth and credit risk a rapid increase in loan portfolios is positively associated with an increase in non-performing loan ratios later on • 2. Loans granted during boom periods have a higher PD than those granted during slow credit growth periods • 3. In boom periods collateral requirements are relaxed while the opposite happens during recessions • Banking supervisors’ concerns are well rooted in empirical grounds • A prudential tool is needed to cope with the potential problems due to too rapid credit growth

39. COUNTRIES ρ ρ ( Δ GDP/LLP/ Loan) - - 0,96** 0,96** UNITED KINGDOM - - 0,79** 0,79** USA - - 0,71** 0,71** SOUTH KOREA - - 0,58 0,58 DENMARK - - 0,58 0,58 FRANCE - - 0,57 0,57 JAPAN - - 0,45 0,45 CANADA - - 0,41 0,41 MEXICO - - 0,40 0,40 HOLLAND - - 0,30 0,30 ITALY - - 0,17 0,17 GERMANY - - 0,10 0,10 Annex-dynamic provisioning • Real policy problems: • Strong credit growth the second half of 90’s • Very low level of loan loss provisions • Moral suasion did not work • Worried about risk taking • Low risk premiums • Expansion in risky sectors • Strong competition among banks SPAIN

40. Annex-dynamic provisioning • To impose a countercyclical LLP • Explicit mechanism that during good times increases general loan loss provisions building up a general loan loss reserve • During bad times, the reserve previously built up is used to cover loan losses • Banks didn’t like it since it hurts the P&L during good times • Smoothing of the P&L, although fully transparent • Banco de España had, and still has, accounting setting powers (i.e. we are responsible for setting the accounting rules for credit institutions)

41. Annex-dynamic provisioning • The so-called statistical provision was compulsory between mid-2000 and end-2004 • Spanish banks built up a loan loss reserve of around 1% of total loans • The loan loss provisions were around 10% of the net operating income • This is NOT a monetary policy tool, it is a prudential tool • It was not designed to control credit growth • It was designed as a prudential tool to cover the potential impact of too rapid credit growth • The rate of credit growth is a bank manager decision

42. Annex-dynamic provisioning • We had to change the system in 2005 as a result of the adoption of the International Financial Reporting Standards (IFRS) by the European Union • We manage to keep some explicit countercyclical mechanism in the LLP, although less marked than before… • …but it was not an easy task… • …and it is still under discussion

43. Annex-dynamic provisioning • IFRS loan loss provisions are very procyclical • Incurred losses, identified individually, increase in bad times • Incurred losses, not yet individually identified evolve also procyclically • Basel II is probably going to be more procyclical • Partly by construction (capital proportional to risk, and risk is procyclical) • Partly because the PIT PDs are very procyclical • Market forces might take into account the increased volatility in lending booms and, hopefully, correct it • All in all, credit cycles might become more volatile and that might hurt the real economy

44. Annex-dynamic provisioning • Supervisors might still have a role to play • Enhanced dialogue with IASB to introduce financial stability concerns • (Retail) depositors might be an interested stakeholder of accounting data also • A more open interpretation of incurred and not yet identified losses • Credit risk increases in good times, shouldn't we increase loan loss provisions then? • Basel II Pilar 2 might be the last resort to cope with financial stability concerns and, in particular, with those related to procyclicality and, more generally, with enhanced volatility of the credit cycle

45. Annex-dynamic provisioning • If IFRS were more flexible it could be possible to address directly the rapid credit growth • The LLP could be based on the credit cycle position of the bank: • the higher the credit growth of the bank, the more it has to provision • the lower the credit growth, the more provisions can liberate from the previously built reserve • In boom periods the LLP would be positive, negative during recessions • The underlying idea is quite simple: • the more rapid credit growth, following our empirical results, the higher the credit risk is assuming the bank and, therefore, the higher the LLP required

46. Annex-dynamic provisioning • where  is the average loan growth rate for the total lending institutions during a credit cycle, Ct-1 the stock of loans the previous period, and C the absolute increase in loans, g covers the latent risk

47. Annex-dynamic provisioning • Mechanism: • While loan growth rates are above the average loan growth rate (=10.09%), the countercyclical provision is positive and the amount charged in the P&L is accrued in a fund • When loan growth is starting to be below the average, the countercyclical provision is negative and it is accrued in the P&L from the cyclical fund previously built • After year 9 the cyclical provision resumes a positive value (new expansionary credit cycle) and the cyclical fund is being built up again

48. Annex-dynamic provisioning

49. Annex-dynamic provisioning • Robust evidence of a positive, although quite lagged, relationship between rapid credit growth and banks’ NPL • In good times riskier borrowers obtain funds and collateral requirements are significantly decreased • Therefore, credit risk increases in good times • Current IFRS does not recognize how credit risk evolves along the business cycle, which means that LLP are very procyclical • Basel 2 might be more procyclical too • More volatile credit cycles? Impact on the real economy?