Testing resilience of the financial system

1. Testing resilience of the financial system

2. Stress testing is simple! • the only things one needs are • a computer to be run by an experienced operator • a couple of friends to discuss assumptions and results

3. Stress testing in the CNB • for assessing resilience of the financial system the CNB conducts regular stress tests of banks since 2003 • three (slightly overlapping) stages in development of the stress testing framework for banks • simple static stress testing/sensitivity analysis (2003-2006) • static stress testing based on (consistent) macroeconomic scenarios, satellite models and interbank contagion (2005-2009) • dynamic model-based stress testing (2009++) • in paralel, the CNB develops since 2008 a liquidity stress testing model for banks that is going to be integrated with the main stress testing framework • since 2007, the CNB conducts stress tests of insurance companies (market risk, insurance-specific risks) and pension funds (market risk)

4. Publication of stress test results • the CNB was always very open in publication and communication of stress test results • traditional means of publication is Financial Stability Report (since FSR 2004 published in January 2005) • results first published in a special feature/article, since FSR 2007 in the main text (chapter Financial Sector – part „assessment of the financial sector‘s resilience“) • Since March 2009, stress tests conducted quarterly (for the CNB macrofinancial panel) and since February 2010, results are regularly published in the CNB website http://www.cnb.cz/en/financial_stability/stress_testing/

5. Stress testing in the CNB Stage I Simple static stress testing/sensitivity analysis (2003-2006) FSR 2004, FSR 2005, FSR 2006

6. Simple static stress testing • methodology stems from the IMF FSAP approach,developed in co-operation with the IMF (Martin Čihák) • for testing credit risk and market risk (interest rate risk and FX risk) • strongly top-down though based on „static“ balance sheets of individual banks and assumptions how balance sheets would change if (a) interest rates, (b) exchange rate, (c) NPL changed • impact horizon of 1 year • suitable for simulations of the impact of • single shocks (sensitivity analysis) like increase of NPLs by 20%, • ad hoc scenarios defined as combination of risk factors that have direct impact on banks‘ balance sheets (interest rate, exchange rate, NPL)

7. Mechanics of the stress test I Transmission of risk factors: • impact of a change (increase) in interest rates: change in net interest income (gap analysis) plus re-pricing of debt securities (duration analysis) • impact of a change in exchange rate: change in value of FX-denominated assets and liabilities (using data on net FX position)plus indirect effect on NPL (loans denominated in FX) • impact of a change in NPL: increase in NPL leads to increase in loan loss provisions (using information about banks‘ provisioning rate)

8. Mechanics of the stress test II Other assumptions: • in the absence of shocks, banks are assumed to generate profit at the level of the average of the last 5 years • profit is used to raise capital to the initial level of capital adequacy (if the profit is sufficient to counterbalance the impact of shocks), the rest (if any) is distributed via dividends • risk-weighted assets (RWA) after shock are calculated as initial RWA minus 80% of the overal impact of shocks • results of the stress tests are presented in percentage points of the initial capital adequacy

9. Ad hoc scenarios in the simple stress test • the CNB used so-called „historical scenarios“ I and II, i.e. combination of shocks that mimic past crisis (1997-1998) and past volatility of variables (see the table taken from FSR 2004) • shocks can be alternatively calibrated for example as 1 p.p. confidence level (roughly 3 standard deviations) • combination of shocks should be plausible and reflect possible reaction of authorities and markets (e.g.. central bank raises interest rates to defend currency from further depreciation etc.)

10. Presentation of results of an ad hoc scenario I (FRS 2005, p. 79)

11. Advantages of simple stress tests I • can be used to quickly assess resilience to specific risks (sensitivity analysis) • respond to questions like „how much would the interest rates have to increase to get post-test capital adequacy equal to the minimum value of 8 % (see chart from FSR 2005)

12. Advantages of simple stress tests II • simple to design and run (xls, no models), in line with FSAP/other IMF missions (facilitates communication with IMF missions) • can be run repeatedly and the results can be compared over time (see chart from FRS 2005) • serve as a necessary first step in developing more comprehensive framework

13. Stress testing in the CNB Stage II Static stress testing based on (consistent) macroeconomic scenarios, satellite models and interbank contagion (2005-2009) FSR 2005, FSR 2006, FSR 2007, FSR 2008/2009

14. Basic building blocks • based on the static simple stress testing, i.e. • same risk factors (interest rate risk, FX risk, credit risk) • same transmission channels (impact on net interest income, repricing of bonds, FX profit/losses, loan loss provisions) • same horizon of 1Y • same assumptions about profit, CAR etc. (but from FSR 2008/2009, pre-provision income instead of profits used) • new features • a new risk factor – interbank contagion • explicit (consistent, i.e. model-generated) macroeconomic scenarios • satellite models to transmit changes in macro variables into risk factors

15. The framework • QPM model (or since late 2008 G3 DSGE model) generates both baseline forecast (the official CNB forecast produced quarterly) as well as alternative „adverse“ macroeconomic scenarios • satellite models are credit growth model (ECM model of aggregated credit growth) and credit risk models (corporate, households)

16. Transmission Channels of Credit Risk • dependent variable of credit risk models: 12M default rate (i.e. new bad loans over initial portfolio) • 12M default rate is also used by commercial banks; the Basel II „PD“ used for IRB approach in credit risk should be „a long-run average of default rates“ • model and explanatory variables • Corporate Sector • Merton model • Macroeconomic shocks (explanatory variables); GDP growth, exchange rate, inflation, debt • Households • Merton model + naive econometric models • Unemployment rate, real interest rates, GDP

17. Credit Risk Modeling • Macroeconomic credit risk model for the Czech and Germany corporates were estimated (Jakubík and Schmieder 2008) • Czech: • German:

18. Credit Risk Modeling • Macroeconomic credit risk model for the Czech and German households were estimated (Jakubík and Schmieder 2008) • Households models: less successful than for corporates, additional (socio-economic) indicators may improve modelling

19. Scenario building • possible to construct scenarios without a macroeconomic model, but to achieve the highest possible consistency, using a macro model (QPM, DSGE, VAR) is of advantage • scenarios should be of a type „low probability – high impact“, but plausible and have some „story“ behind • should react to risks identified in risk assessment; in case of double-sided risk, opposite scenarios can be built (e.g. appreciation/depreciation, increase/decrease in interest rates) • the story can be reflected in the name of the scenario (makes it easier to remember); „sexy“ names are of advantage • use baseline scenario (official forecast) as benchmark; however, problems with interpreting the results if the stress testing model/models calibrated conservatively

20. Example FSR 2007: stress test scenarios • Three alternative model-consistent scenarios in FSR 2007 (scenarios for the year 2008 • A - safe haven (appreciation of currency) • B - property market crisis (internal shock with direct impact on banks) • C - loss of confidence (external shock – increase in risk aversion)

21. FSR 2007: Bank Stress Test Scenarios • all the scenarios were defined primarily by the evolution(change) of key macroeconomic indicators such as GDP, inflation, the unemployment rate, short-term interest rates and the exchange rate

22. FSR 2007: Impact of Alternative Scenarios on the Banking Sector • Example of presentation of the results • The results were interpreted as follows: • The banking sector seems to be resilient to a wide range of risks • Only an extreme macroeconomic scenario would necessitate capital injections to maintain sufficient capitalization

23. FSR 2007: Impact of Alternative Scenarios on the Banking Sector • alternative – graphic – presentation of the results • scenario C would have the strongest impact on banking sector

24. Example of sensitivity analysis: the role of real estate prices (FSR 2007) • Simple sensitivity analysis within a scenario stress testing possible; in FSR 2007, we looked at sensitivity of banks to real estate prices • Radical assumption: increase in the share of NPLleads to a fall in real estate prices of the same extent • Test demonstrated the banking sector’s high resilience to a mortgage loan portfolio shock • This is due to very conservative LTV ratio around 50 %

25. Another sensitivity test within a scenario: the role of interest rate risk (FSR 2007) • The sensitivity analysis - capital adequacy of the banking sector would fall below the regulatory minimum if short-term interest rates rose by more than 4.4 percentage points

26. Example FSR 2008/2009: macroeconomic scenarios • Three scenarios reflecting the risks from the global financial crisis • Europe in recession (= baseline prediction) • Nervousness of the markets (a la „loss of confidence“, i.e. increase in risk aversion) • Economic depression (very large decline in GDP)

27. FSR 2008/2009: capital adequacy looks satisfactory even in large depression • Horizon of stress tests is just one year. • In a longer horizon, the NPL share continues to grow and capital adequacy deteriorates further. • Still, unless recession is very long and very deep, the banks should manage without public funds.

28. FSR 2008/2009: presentation of the results • same style of presentation • information about the capital injections needed

29. Stress testing in the CNB Stage III Dynamic model-based stress testing (2009++) FSR 2008/2009; FSR 2009/2010

30. Problems with static stress testing • The 2005-2009 framework limited as regards its ability to • capture the effects of credit, interest and currency shocks over time in a more dynamic way, • analyze the impact of shocks in a longer horizon than a one-year horizon (up to two to three years), • estimate the pre-provision income as a function of both the macroeconomic development and a bank’s business model, • be expressed in the variables used in current regulatory framework (PD, LGD) and thus mimick the stress testing done by individual banks within Pillar II of Basel II • capture pro-cyclical nature of current Basel II regulation, • integrate fully the funding liquidity shock within the macroeconomic stress testing framework, • link the interbank contagion and second-round liquidity shocks to development of the individual bank’s capital and liquidity conditions in a non-linear way, and • capture potential two-way interaction between the banking system and the macroeconomic environment (feedback effect).

31. Example of the „time“ problem: market vs credit risk • difference in time horizon between the effects of market and credit risks • impact of a change in interest rates or other market variables (the exchange rate or stock prices) on the balance sheets of financial institutions is virtually immediate (revaluation of securities) • credit risk accumulates over a longer time frame (one to three years) as loans gradually shift into the NPL category • the „Phase II“ CNB stress testing framework addressed this discrepancy with a compromise assuming an impact horizon of one year • macro variables of the projected year were averaged to produce the „shock“ as the difference between initial and average future value = underestimates peaks in possible crisis (Lehman September 2008)

32. Example of the evolution of the impact of shocks • scenario „nervousness of markets“ from the FSR 2008/2009 assumed losses due to unfavourable interest rate changes insome quarters, but these losses are fully reversed in the following periods • this dynamics of the directional changesin the shocks over time generates stress situations in the financialsector that cannot be captured bythe standard stress tests using averages for the entire test period.

33. Solution: move to „dynamic stress testing“ • modelling of the banking sector (see Aikman et al. 2009) • banks’ balance sheets would be modelled dynamically, for example for each quarter, as they are hit by the individual shocks • this would allow the shock impact horizon to be extended, for example to six to eight quarters • losses would accumulate gradually • further satellite models needed (for etc.) • pre-provision income • other risk parameters (property prices, LGD, yield curve) • threshold model for integration of liquidity shock and interbank contagion • if any of the key variables (e.g. the capital adequacy ratio) overstepped a pre-defined threshold, other shocks would be generated (e.g. interbank contagion, outflow of liquidity)

34. Scheme of dynamic stress testing Network model (interbank contagion)

35. Current framework of the dynamic stress tests • CNB now performs stress tests with every new quarterly macroeconomic forecasts (i.e. 4 times a year – February, May, August and November) • alternative macro scenarios: one scenario reflects actual CNB‘s macroeconomist forecast, one or two adverse scenarios run in DSGE model are outlined by the financial stability team together with modelling division experts (14 variables used), • the horizon is set to 8 quarters – for example August 2010 stress tests performed on mid-2010 portfolios with August 2010 forecasts focused on horizon 3Q2010 – 2Q2012.

36. Dynamic features of CNB‘s stress tests • Tests are set as dynamic – for every item in assets, liabilities, income and costs there is an initial state to which the impact of shocks is added in one quarter and the results serve as the initial state for following quarter • this is repeated in next 8 quarters for which the prediction is generated. • Four risks are tested: credit risk, interest rate risk, currency (FX) risk and interbank contagion • Conservative calibration of stress test parameters (slight overestimation of risks, slight underestimation of buffers)

37. Bringing the stress tests in line with Basel II • Pillar I: change in credit risk terminology/risk factors • explicit PD (default rates), LGD, EL (expected loss) • loan segments very close to Basel II segments (corporate, retail, other) • for banks in IRB approach, application of Basel II formula to determine capital requirements • Pillar II: exchange of views with banks on stress testing methodology • adjustments in interest rate impact (use of derivatives, interest rate sensitivity of current accounts etc.) • explicit (expert) modelling of yield curve

38. Credit risk I • the methodology is being continuously improved; the tests work with four separate loan portfolios: non-financial corporations,households – consumer, households – mortgages,other loans Two impacts o credit risk: • Expected loss (EL) • PDxLGDxEAD • PD is a result of satellite models (dependent variable; smoothed default rate df), LGD set expertly (or via simple models) • EAD is non-defaulted stock of exposures; total exposure modelled via credit growth model(s) • Risk-weighted assets (RWA) • IRB formula using PD, LGD and EAD • not precise (non-linearity, not all banks have IRB approach for credit risk management), but close to how banks behave

39. NPLs NPL ratio - the ratio of non-performing loans to total loans • product of PD/df, existing NPLs, stock of loans (L) and outflow of NPLs outof the balance sheets NPL(2)/L(2) = approx. [NPL(1) + L(1)*df - a*NPL(1)]/L(2) • expert judgment/assumptions about NPL outflow (parameter a of around 15% in a quarter): • parameter a may change during bad times, very difficult to model

40. Illustrative example of credit shock impact: expected loss/provisions, NPL and RWA Calculation of credit losses Note: quarterly PDs, yearly PDs = 4 x 3% = 12% Impact on RWA For simplicity: 0% credit growth assumed New NPLs (0,03 x 1000) NPL outflow (assumed 15% each quarter)

41. CNB‘s LGDs: first expert estimate in July 2009 versus adjustment in 2010 Parameter LGD Since May 2010 (FSR 2009/2010), simple models for „elevated“ LGDs (role of GDP, property prices and unemployment)

42. Example of IRB formula • Impact of macro stress tests on IRB minimum capital requirements (CR) for a hypothetical portfolio (CR are measured in % of exposure) • Taken from Jakubík and Schmieder (2008) • The quantiles of all macroeconomic variables change by 10 percentage points (moderate stressscenario, HS 10%) and 20 percentage points (severe stress scenario, HS 20%), respectively, inthe unfavourable direction.

43. Potential „deleveraging“ leads to higher CAR in worse scenario (protracted recession in July 2009 tests). Thus, in bad times, there are two competiting drivers of RWA PD, LGD – push RWA upwards Stock of exposures – push RWA downwards For comparison a scenario with positive credit growth (and higher PD, LGD): negative impact on CAR confirmed (via higher RWA) Credit growth, RWA & capital adequacy (CAR)

44. How to work with pre-provision income, profits and capital • until June 2010 (FSR 2009/2010), pre-provision income was expertly set at x % of average of past 2 years (x < 100%, thus additional stress applied in the sense of lower intermediation activity) • during 1H2010, a simple model of pre-provision income was estimated (the main determinants: nominal GDP, yield curve, NPLs and capital adequacy) • profit/loss is generated using the pre-provision income and the impact of shocks • regulatory capital is adjusted every 2Q to get back to initial CAR • thus, a P/L account and balance sheet of all banks generated every quarter = possible to cross-check with reality later on

45. Modelling pre-provision income • comparison of model estimation versus expert setting of pre-provision income • conservative estimation – estimate of the model minus 1 stdev of growth

46. Net income, P/L and capital adequacy: an example • For final evaluation of banks‘ resilience capital adequacy is estimated. • Link between shocks impact and capital adequacy must reflect • (net) income generated by banks even under stress, • asymmetric treatment of profits in calculation of regulatory capital, • topping up of regulatory capital (set for 2nd calender quarter every year).

47. Regular cross-check of the stress testing framework I • regular consultations with commercial banks on stress testing methodology • project of „joint stress tests with selected banks“ • basically bottom-up stress tests – CNB gives the increase in risk parameter PD, banks themselves calculate the impact • since summer 2009, currently third round completed • aggregate results published in the FSR 2009/2010

48. Regular cross-check of the stress testing framework II • verification of the models and assumptions (over time, banking sector changes thus the stress testing framework should react as well - Basel II, use of derivatives etc.) • Geršl, A. – Seidler, J.: Stress test verification as part of an advanced stress-testing framework. CNB, FSR 2009/2010 • use baselines, but assymetric assessment needed (better to overestimate risks than underestimate) • conservative calibration of models needed

49. Presentation of results: FSR 2009/2010, aggregate results • A slight change in the way of presentation • as a simplified profit/loss account • capital injection needs expressed verbally in the text

50. Presentation of results: FSR 2009/2010, capital adequacy and NPLs • In quarterly publication, we present (see the CNB website) • charts on main macro variables (GDP, inflation, exchange rate and 3M interbank rates), • charts on NPLs development and • chart on capital adequacy development • In FSR, further suplementary charts available (such as provisioning etc.)