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Testing Resilience of the Czech Financial System Smilovice 2009 Jan Frait Deputy Head of Economic Research and Financial Stability Department.

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Presentation on theme: "Testing Resilience of the Czech Financial System Smilovice 2009 Jan Frait Deputy Head of Economic Research and Financial Stability Department."— Presentation transcript:

1 Testing Resilience of the Czech Financial System Smilovice 2009 Jan Frait Deputy Head of Economic Research and Financial Stability Department

2 2 for assessing resilience of the financial system to shocks other than liquidity shocks, the CNB conducts stress tests of banking sector 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 with some interbank contagion (2005-2009)  dynamic model-based stress testing (2009++) Macro-stress tests have semi-top-down style, CNB also runs bottom-up tests with banks since 2007, the CNB conducts stress tests of insurance companies (market risk, insurance-specific risks) and pension funds (market risk) Stress testing in the CNB

3 3 the CNB was always very open in communication of stress test results to the industry and public 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“) stress tests conducted nowadays quarterly (for the CNB macrofinancial panel),  results sometimes published in Bank Board members‘ presentations,  now ready for regular release of results starting from next quarterly exercise (February 2010) Publication of stress test results

4 4 Stage I Simple static stress testing/sensitivity analysis (2003-2006) FSR 2004, FSR 2005, FSR 2006 Stress testing in the CNB

5 5 Simple static stress testing methodology based on the IMF FSAP approach, developed in co-operation with the IMF for testing credit risk and market risk (interest rate risk and FX risk) 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)

6 6 Mechanics of the stress test I Transmission of risk factors: impact of a change in NPL: increase in NPL leads to increase in loan loss provisions (using information about banks‘ provisioning rate) 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)

7 7 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

8 8 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.)

9 9 Presentation of results of an ad hoc scenario I (FRS 2005, p. 79)

10 10 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) served as a necessary first step in developing more comprehensive framework

11 11 Stage II Static stress testing based on (consistent) macroeconomic scenarios and satellite models (2005-2009) FSR 2005, FSR 2006, FSR 2007, FSR 2008/2009 Stress testing in the CNB

12 12 based on the static simple stress testing, i.e.  same risk factors ( credit risk, interest rate risk, FX risk)  same transmission channels (impact on net interest income, revaluation 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  explicit (consistent, i.e. model-generated) macroeconomic scenarios  satellite models to transmit changes in macro variables into risk factors  a new risk factor – interbank contagion Basic building blocks

13 13 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)

14 14 Transmission Channels of Credit Risk 14 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

15 15 Transmission Channels of Credit Risk 15 other parameters entering the stress tests were derived using sub-models and expert estimates NPL ratio - the ratio of non-performing loans to total loans - was generated using  credit risk models  credit growth model  expert judgment/assumptions about NPL outflow a NPL(2)/L(2) = approx. [NPL(1) + L(1)*df - a*NPL(1)]/L(2) approximate relationship, because it depends on the time horizon (new loans can also turn bad)

16 16 Credit Risk Modeling 16 Macroeconomic credit risk model for the Czech and Germany corporates were estimated (Jakubík and Schmieder 2008) Czech: German:

17 17 Credit Risk Modeling 17 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

18 18 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 typ „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 Scenario building

19 19 Example FSR 2007: stress test scenarios 19 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)

20 20 FSR 2007: Bank Stress Test Scenarios 20 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

21 21 FSR 2007: Impact of Alternative Scenarios on the Banking Sector 21 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

22 22 FSR 2007: Impact of Alternative Scenarios on the Banking Sector 22 alternative – graphic – presentation of the results scenario C would have the strongest impact on banking sector

23 23 Example FSR 2008/2009: macroeconomic scenarios 23 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)

24 24 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.

25 25 FSR 2008/2009: presentation of the results same style of presentation information about the capital injections needed

26 26 Stage III Dynamic model-based stress testing (2009++) FSR 2008/2009 Stress testing in the CNB

27 27 Existing framework limited as regards its ability to  analyze the impact of shocks in a longer horizon than a one-year horizon (up to two to three years),  capture the effects of credit, interest and currency shocks over time in a more dynamic way,  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). Problems with static stress testing

28 28 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 existing CNB stress testing framework was addressing 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) Example of the „time“ problem: market vs credit risk

29 29 „experimental“ dynamic stress test in FSR 2008/2009 scenario „nervousness of markets“ assumes losses due to unfavourable interest rate changes in some quarters, but these losses are fully reversed in the following periods this dynamics of the directional changes in the shocks over time generates stress situations in the financial sector that cannot be captured by the standard stress tests using averages for the entire test period. Example of the evolution of the impact of shocks

30 30 Pillar I: change in credit risk terminology/risk factors  explicit PD (probabilities of default, proxied by default rates), LGD (loss given default), 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 and interest rate risk Bringing the stress tests in line with Basel II

31 31 Interest rate shock revised The new framework for assessing the impact of interest rate shock  partially set following the research of banks practices regarding interest risk management  assumption that only on some part (cca 20 %) of short-term liabilities (mostly sight deposits) banks adjust client rates according to the money market  the change in value of bonds is muted for banks using hedging via IRS  revaluation of long-term bonds is calculated with a forecast of 5Y rate which is linked to 1Y rate, 5Y Bund rate and assumption on risk premium (spread between 5Y euro rate and 5Y CZK rate)

32 32 regular consultations with commercial banks on stress testing methodology 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.)  how to assess results of verification?  use baselines, but assymetric assessment needed (better to overestimate risks than underestimate)  in crisis periods, alternatives might be better benchmarks than baselines  conservative calibration of models and/or additional expert‘s adjustments are needed Regular cross-check of the stress testing framework

33 33 Last Stress Testing Exercise – Assumptions and Results Stress testing in the CNB

34 34 Basic framework of CNB‘s stress tests This part will focus on methodology and some results of stress tests of the Czech banking sector. CNB now performs stress tests with every new quarterly macroeconomic forecasts (i.e. 4 times a year)  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 - actual (internal) stress tests performed on mid-2009 portfolios with July 2009 forecasts focusing on horizon 3Q2009 – 2Q2011. The results presented below are taken from July 2009 exercise (to some extent „work in progress“ as a part of dynamic stress testing methodology development. 34

35 Two macroeconomic scenarios The July bank stress test worked with two scenarios:  Scenario A: “baseline” reflects the CNB’s July forecast  Scenario B: “protracted recession” expects a greater and longer decline in GDP compared to the baseline scenario These scenarios were updates to the scenarios published in the Financial Stability Report in June 2009 35

36 36 Two macroeconomic scenarios 36

37 37 Credit risk in CNB‘s stress tests Credit risk and credit growth assumptions: outputs of satellite models utilizing macro scenarios (4 separate loan portfolios modelled). 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“ NPL ratio - the ratio of non-performing loans to total loans - generated using expert judgment/assumptions about NPL outflow (15% in a quarter):  NPL(2)/L(2) = approx. [NPL(1) + L(1)*df - a*NPL(1)]/L(2) 37

38 38 Dynamic features of CNB‘s stress tests Credit growth is estimated for each portfolio via simple macroeconomic model. Forecast of default rates and credit dynamics are transformed to predictions of main balance sheet and flow variables of banks. Four key risks are tested then (credit, interest rate, currency and interbank contagion). 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. 38

39 39 Credit developments in two scenarios 39

40 NPLs in major segments (corporates, households) higher relative to FSR 2008/2009...... due to higher predicted default rates  corporates - 2009/20010 - baseline 11,8/11,1 % - adverse 13,4/11,6 %  households - 2009/2010 - baseline 5,7/6 % - adverse 7/9 % NPLs in current stress tests 40

41 Assumptions regarding behaviour of net income, profits and regulatory capital  pre-provision income is expertly set at x % of average of past 2 years (x < 100%, thus additional stress applied in the sense of lower intermediation activity). Profit/loss is generated using the pre-provision income and the impact of shocks  in current accounting period pre-provision income serves as a first line „buffer“ against the impact of shocks,  only after the „buffer“ is exhausted, the impacts are deducted from the capital. Regulatory capital is adjusted every 2nd Q of calendar year to get back to initial CAR, if there are sufficient profits generated in previous accounting year...... thus, a P/L account and balance sheet of all banks generated every quarter = possible to cross-check with reality later on. How we work with pre-provision income, profits and capital 41

42 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 in 2nd Q. 42

43 Current ST results in detail Strong resilience confirmed despite recessionary scenarios. CAR higher than at the end of 2008 (effect of high capital buffer + relatively strong income generation capacity) 43

44 Banks remain stable in both scenarios. Net income (profit prior to shocks): 90 (70) % of previous 2Y average in baseline (in protracted recession). Potential „deleveraging“ leads to higher CAR in protracted recession. For comparison a scenario with credit growth constructed too: negative impact on CAR confirmed. Current ST: capital adequacy 44

45 …. the banking sector remains strong … so far The Czech banking sector has remained profitable and the profits have not shown a tendency to decline thus far. Capital adequacy went up as banks retained a large part of generated profit as a buffer against the expected increase in credit risk. 45

46 46 Thank You for Your Attention! Contact: Financial Stability Team in the CNB Contact: Jan Frait Czech National Bank Na Prikope 28 CZ-11503 Prague Tel.: +420 224 414 430 E-mail: Financial Stability Team in the CNB

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