Testing resilience of the financial system

2

• the only things one needs are • a computer to be run by an experienced operator• a couple of friends to discuss assumptions and results

Stress testing is simple!

3

• 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)

Stress testing in the CNB

4

• 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 websitehttp://www.cnb.cz/en/financial_stability/stress_testing/

Publication of stress test results

5

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

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

• 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

Basic building blocks

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

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Credit Risk Modeling

• Macroeconomic credit risk model for the Czech and Germany corporates were estimated (Jakubík and Schmieder

2008)

• Czech:

• German:

Variable Notation

Constant -3.060*** 0.358 -2.6997*** 0.07141

Nominal Interest Rate (b 1 ) ir NA -1 2.2194*** 0.4919

Real Exchange Rate (b 2 ) e -2 1.062*** 0.323 NA

Inflation (b 3 ) p -1 -4.850*** 0.636 NA

GDP (b 4,1 ) gdp 0 -4.609*** 1.079 0 -3.3677*** 0.328

Industry production (b 4,2 ) indprod NA -3 -0.8215*** 0.1464

Credit-to-GDP ratio (b 5 ) debt -4 3.006*** 0.246 -4 1.0871*** 0.1213

Dummy variable (b 6 ) dum 0 0.238*** 0.043 0 0.0400*** 0.0125Significance level: **: Significant at 5% level; ***: Significant at 1% level;

Czech case German caseLag Coefficient Std. error Lag Coefficient Std. error

)( 6451,41322 tttttt dumdebtgdpecdf bbbpbb

)( 4532,41,411 ttttt debtindprodgdpircdf bbbb

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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

Variable Notation

Lag Coefficient Std. error Lag Coefficient Std. errorConstant -2.224*** 0.071 -5.5656*** 0.1072Household Income (b1) inc NA NA NA 0 -5.7912*** 0.9244

Credit-to-GDP ratio (b2) debthouse NA NA NA -4 5.7186*** 0.2788

Unemployment Rate (b3,1) u -4 3.695*** 0.846 NA NA NA

Real Interest Rate (b3,2) r -3 1.808** 0.596 NA NA NA

Significance level: **: Significant at 5% level; ***: Significant at 1% level;

Czech case German case

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• 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

Scenario building

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)

Calibration of baseline and alternative scenarios(2008 averages)

Baseline Scenario A Scenario B Scenario CReal GDP growth (%; y-o-y) 4.1 2.4 0.3 2.8Inflation rate - CPI (%; y-o-y) 6.2 7.0 5.3 8.0Unemployment rate (%) 6.0 6.3 6.7 6.31Y PRIBOR (%) 3.8 2.8 1.5 8.7

CZK/EUR exchange rate ... 1) 25.6 27.0 30.5Source: CNB

Note: 1) In 2008, the baseline expects an correction of the record values initially and then a slight appreciation

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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

Scenario type and shock size in bank stress testScenario type Baseline Scenario A Scenario B Scenario CChange in CZK interest rates -0,2 p.p. 0,1 p.p. -0,9 p.p. 4,4 p.p. Change in EUR interest rates -0,8 p.p. -1,4 p.p. -0,4 p.p. -0,4 p.p.

Change in CZK/EUR exchange rate (- appreciation)

- -6.7% -0.4% 20.1%

Loan default rate 4.2% 5.2% 6.9% 4.9%Total credit growth 16.4% 9.9% 14.6% 4.9%Change in property prices (+ rise, - fall)

15% 0% -30% -5%

Note: Changes in parameters represent the difference between 2007 Q4 and 2008 Q1, or, in the case of the baseline, between 2007 Q4 and the average for 2008.

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

Results of bank stress tests(capital adequacy; % and p.p.)Scenario type Baseline Scenario A Scenario B Scenario C

2007 2007 2007 2007

Capital adequacy (CAR) 1) 11.5 11.5 11.5 11.5Results for chosen scenario type Overall impact of shocks (p.p. CAR) -2.1 -2.8 -3.0 -6.3 Interest rate shock 0.2 0.1 0.6 -2.6 Exchange rate shock -0.1 -0.2 0.0 0.5 Credit shock -2.0 -2.4 -3.3 -3.6 … households -0.5 -0.5 -0.5 -0.5 … non-financial corporations -1.0 -1.5 -2.0 -0.6

Interbank contagion2) -0.2 -0.2 -0.2 -0.7 CAR before profit allocation 9.4 8.7 8.5 5.2

Profit allocation (p.p. CAR)3) 1.8 2.3 2.2 2.8Post-shock CAR 11.3 11.0 10.8 8.1

Capital injection (% of GDP)4) 0.0 0.1 0.1 1.1

Share of banks with negative capital after shock5) 0.0 0.0 0.0 14.9Notes:

1) CAR means the capital adequacy ratio defined in accordance with the relevant CNB regulations (in particular those

governing the capital adequacy of banks and other prudential business rules).

2) Test integrated with interbank contagion and expected level of loss given default (LGD) 100%

and chosen probability of the banks' failure (default) on the basis of the CAR.

3) The scenarios assume that in the absence of shocks each bank would generate profit (or loss) equal to the average for the previous five

years and that it would use any profit (income) as a first line of defence against a declining CAR.

4) The capital needed to ensure that each bank has a post-shock CAR of at least 8%.

5) Market share of banks with negative capital after the impact of the assumed shocks (as a percentage of total assets).

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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

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2

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6

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12

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18

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24

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2006 2007 2008

Capital adequacy (%)

Growth of total loans (%)

Baseline ____Scenario A ____Scenario B ____Scenario C ____

Share of default loans(%)

Note: Growth in total loans is defined as the average annual rate of growth. The share of new non-performing loans (NPLs) relates to the estimation of the loan volume at the end of 2007.

Source: CNB

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 NPL leads to a fall in real estate prices of the same extent Simple test for mortgage loans

(%; 2007)

0

4

8

12

0 10% 20% 30% 40%

Growth of default mortgages and decline in real estate prices

Pre-shock CAR

CAR after credit shock

CAR after credit shock and sale of collateral at a loss

CAR after credit shock and sale of collateral (incl. profit allocation)

Note: Scenarios of additional defaults as 10-40% of mortgage loans becoming default loans. Banks or clients would sell collateral at 90-60% value.

Source: CNB

• 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

Smooth change in interest rate for selected scenario(capital adequacy; %; 2007)

-4-202468

10121416

0% 0.5% 1% 1.5% 2% 2.5% 3% 3.5% 4% 4.5%

Banks Insurers Pension funds

Source: CNBNote: Calculation for scenario C.

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)

Stress-Test Scenarios

in CNB's Financial Stability Report (June 2009)

Development of key macroeconomic variables in 2009Real GDP (%, y-o-y) -2.4 -3.9 -6.2Inflation (%, y-o-y) 1.2 1.7 1.3Interest rate 1Y PRIBOR (%) 2.4 4.6 2.6Exchange rate CZK/EUR 26.6 28.8 27.8

Europe in recession

Nervousness of markets

Economic depression

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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.

Results of stress testing scenarios

(%; banking sector)

Source: CNB

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2

4

6

8

10

12

14

16

06/2007 12/2007 06/2008 12/2008 06/2009 12/2009

Europa in recession


Economic depression

Capital adequacy development (%)

Share of non-performing loans (%)

28

FSR 2008/2009: presentation of the results

• same style of presentation

• information about the capital injections needed

Results of bank stress tests

(capital adequacy; % and p.p.)Scenario type

Capital adequacy (CAR) at the end 2008 1/ 12.3 12.3 12.3

Overall impact of shocks (p.p. CAR) -3.2 -5.4 -5.0 Interest rate shock 1.3 0.0 1.2 Exchange rate shock 0.0 0.1 0.1 Credit shock -4.4 -5.4 -6.2 … households -1.3 -1.5 -1.8 … non-financial corporations -3.0 -3.1 -3.9 Interbank contagion -0.1 -0.1 -0.1

Income allocation 2/ 2.2 3.1 2.4

Post-shock CAR 11.3 10.0 9.7

Capital injection (CZK billions) 3/ 8.0 15.7 15.5

Capital injection (% of GDP) 3/ 0.2 0.4 0.4

Number of banks (CAR below 8 %) 4/ 4 8 4

Share of banks (CAR below 8 %) 4/ 8.2 21.8 5.0

Number of banks with negative capital 5/ 0 0 1

Share of banks with negative capital 5/ 0.0 0.0 5.2

Europe in recession


Economic depression

1/ CAR means the capital adequacy ratio defined in accordance with the relevant CNB regulations (in particular those governing the capital adequacy of banks and other prudential business rules).2/ We assume that banks would generate income in all adverse scenarios which would be used to strengthen the capital. The level of income for individual banks is estimated using the past development of income and parameters of the scenario. Every bank allocates the income to reach the starting CAR level.3/ The capital needed to ensure that each bank has a post-shock CAR of at least 8%.4/ Banks with post-shock CAR 0 - 8 %.5/ Banks with post-shock negative capital.

29

Stage III

Dynamic model-based stress testing

(2009++)

FSR 2008/2009; FSR 2009/2010


30

• 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).

Problems with static stress testing

31

• 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)

Example of the „time“ problem: market vs credit risk

32

• scenario „nervousness of markets“ from the FSR 2008/2009 assumed 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

33

1. 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

2. further satellite models needed (for etc.)• pre-provision income• other risk parameters (property prices, LGD, yield curve)

3. 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)

Solution: move to „dynamic stress testing“

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

• 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

Bringing the stress tests in line with Basel II

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:1. 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)2. 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

Default loansNon-default

portfolio (NP)50 + 30 - 0,15x50

= 72,51000 - 30 = 970 6.9% function (970; PD; LGD) 12,5 x KP

Capital requirements(KP)

RWA

Final state

Exposure in bil. CZKNPL ratio

Default loansNon-default

portfolio (NP)in % NP bil. CZK

50 1000 4.8% 3% 45% 1.4% 14

Exposure in bil. CZK Loss (PD x LGD)

Initial state Parameters Impact calculation

NPL ratioPD

(quarterly)LGD

Illustrative example of credit shock impact: expected loss/provisions, NPL and RWA

Calculation of credit losses

Impact on RWA

New NPLs (0,03 x 1000)NPL outflow (assumed 15% each quarter)

For simplicity: 0% credit growth assumed

Note: quarterly PDs, yearly PDs = 4 x 3% = 12%

• CNB‘s LGDs: first expert estimate in July 2009 versus adjustment in 2010

Parameter LGD

"baseline" protracted recessionCorporates 45% 55%Households housing credit 10% 20% consumer credit 45% 70%Other clients 45% 45%

LGDs for baseline

Corporates 45%

Households

housing credit 20%

consumer loans 55%

Other clients 45%Property price index and LGD for house purchase loans

(loss of confidence scenario, FSR 2009/2010)(2007 Q4 = 100; LGD in %)

Source: CNB, CZSO, CNB calculation

60

70

80

90

100

110

120

03/08 09/08 03/09 09/09 03/10 09/10 03/11 09/11 03/12

0

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20

30

40

50

60

Property price index LGD (right-hand scale)

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)

Stress Scenario

End 2006 portfolio

(unstressed) PD stress only PD and LGD stress(case 1) (case 2)

Corporate-PD (%) 3.5

Household-PD (%) 2.59

HS 10% LGD (%) 45 45 54

Czech Capital Requirements (%) 7.82 8.66 (+10.7%) 10.39 (+32.9%)Republic Corporate-PD (%) 3.5


HS 20% LGD (%) 45 45 54

Capital Requirements (%) 7.82 10.37 (32.6%) 12.45 (59.2%)Corporate-PD (%) 1.43


HS 10% LGD (%) 45 45 54

Germany Capital Requirements (%) 4.66 4.87 (+4.5%) 5.84 (25.3%)Corporate-PD (%) 1.43


HS 20% LGD (%) 45 45 54

Capital Requirements (%) 4.66 5.09 (+9.2%) 6.11 (31.1%)

0.128

1.85

0.148

2.69

10.6

2.75

1.63

Parameter Forecasted 2007 stress portfolio

5.5

• The quantiles of all

macroeconomic

variables change

by 10 percentage

points (moderate

stress scenario,

HS 10%) and 20

percentage points

(severe stress

scenario, HS

20%), respectively,

in the unfavourable

direction.

• 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)

Capital ratio: model-based (negative) credit growth(in %)

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Baseline Protracted recession

Capital ratio: 8% credit growth assumption(in %)

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16

06/07 12/07 06/08 12/08 06/09 12/09 06/10 12/10 06/11

Baseline Protracted recession

44

• 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

How to work with pre-provision income, profits and capital

45

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

Modelling pre-provision income

Outturn versus model estimate of adjusted operating profit on the past(quarterly values in CZK billions; seasonally adjusted)

Source: CNB, CNB calculation

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25

12/03 12/04 12/05 12/06 12/07 12/08 12/09

Model estimate on past Outturn

Estimate of adjusted operating profit for each scenario(CZK billions; seasonally adjusted)


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06/08 12/08 06/09 12/09 06/10 12/10 06/11 12/11Baseline ScenarioReturn of RecessionLoss of ConfidenceOriginal estimate for Baseline Scenario: 90% of averageOriginal estimate for Return of Recession: 80% of averageOriginal estimate for Loss of Confidence: 70% of average

Regulatory capital

RWA CARLoss from

shock impactNet

incomeP/L

Regulatory capital

RWA CAR

Example 1 100 1000 10.0% 20 30 +10 100 1020 9.8%

Example 2 100 1000 10.0% 40 30 -10 90 1020 8.8%

Final stateEstimate of P&L over quarterInitial state

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).

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• 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

Regular cross-check of the stress testing framework I

(EAD weighted; %)

Baseline scenario

Adverse scenario

PD(%)

LGD(%)

PD(%)

PD(%)

Corporate exposure categories 2,65 41,34 3,62 5,62

Large enterprises 1,77 41,19 2,42 3,76

Small and medium-sized enterprises (SMEs) 3,54 40,66 4,81 7,43 Specialised lending 2,95 44,00 4,12 6,48Retail exposure categories 3,00 33,08 3,54 4,48 Retail-assessed SMEs 3,49 45,78 4,89 7,60 Loans for house purchase 2,15 19,76 2,36 2,80 Other loans to individuals 4,48 53,93 5,29 6,48

Source: CNB

Approximate rise in PD for individual portfolios

Actual situation as of31 Dec. 2009

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• 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

Regular cross-check of the stress testing framework II

Verification of CAR estimate(CAR in %; estimate for 1-year horizon)

8

9

10

11

12

13

14

15

04/Q4 05/Q3 06/Q2 07/Q1 07/Q4 08/Q3 09/Q2

Reality Prediction Prediction – known macro

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• A slight change in the way of presentation• as a simplified profit/loss account• capital injection needs expressed verbally in the text

Presentation of results: FSR 2009/2010, aggregate results

Impact of the alternative scenarios on the banking sector

2010 2011 2010 2011 2010 2011

Expected credit losses

CZK billions -40,6 -26,1 -56,1 -48,2 -48,1 -68,8

% of assets -1,0 -0,6 -1,3 -1,1 -1,1 -1,7

Profit/loss from market risks

CZK billions 4,8 -1,8 0,6 -1,5 -17,9 2,3% of assets 0,1 0,0 0,0 0,0 -0,4 0,1

Interbank contagion

CZK billions -0,1 0,0 -0,4 -1,3 -0,3 -1,6% of assets 0,0 0,0 0,0 0,0 0,0 0,0

Earnings for covering losses(adjusted operating profit)

CZK billions 83,3 90,6 75,3 59,2 85,5 70,8% of assets 2,0 1,9 1,8 1,4 2,0 1,7

Pre-tax profit/lossCZK billions 47,3 62,6 19,4 8,3 19,1 2,6% of assets 1,1 1,3 0,5 0,2 0,5 0,1


Baseline Scenario Return of Recession Loss of Confidence

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• 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.)

Presentation of results: FSR 2009/2010, capital adequacy and NPLs

Risk costs of the banking sector in each scenario

(provisioning as % of gross loans for given year)


0,0

0,5

1,0

1,5

2,0

2,5

3,0

3,5

2009 BaselineScenario

Return ofRecession

Loss ofConfidence

2010 2011

Capital adequacy ratios in each scenario

(%)

Source: CNB,CNB calculation

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9

10

11

12

13

14

15

16

03/08 09/08 03/09 09/09 03/10 09/10 03/11 09/11 03/12Baseline Scenario Return of Recession

Loss of Confidence

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• Test of concentration of credit portfolios• collapse of three largest borrowers in each bank

• Test of decline in value of certain asset class • decline in the value of exposures to Greece, Spain, Portugal and Italy by 50 % or

100 %

Ad-hoc tests in FSR 2009/2010

Impact of the collapse of the three largest debtors of each bank

(in the Loss of Confidence scenario)(%)

Source: CNB

0

1

2

3

4

5

6

Without collapse ofthree largest debtors

With collapse of threelargest debtors

(LGD=45%)

With collapse of threelargest debtors(LGD=100%)

0

2

4

6

8

10

12

14

Loan losses in 2010 (%)

End-2010 CAR (%; right-hand scale)

Impact of the ad-hoc "propagation of Greek crisis" test

(in the Loss of Confidence scenario)(%)


0

1

1

2

2

3

3

Original Loss ofConfidence

With losses fromexposures to risky

countries (LGD = 50%)

With losses fromexposures to riskycountries (LGD =

100%)

8

9

10

11

12

Credit and market losses in 2010 (% of assets)

End-2010 CAR (%; right-hand scale)

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• Reverse logic: we are looking for values of parameters/macro developments that would bring the banking system as a whole to the limit of 8 % CAR

• In the Czech Republic, the GDP growth would have to be around -6.5% in 2011 to cause a serious problem for banks

Reverse stress test

Results of reverse stress test

(%; for Return of Recession scenario)


-10

-5

0

5

10

15

20

03/08 09/08 03/09 09/09 03/10 09/10 03/11 09/11 03/12Original GDP pathAdjusted GDP pathCAR: originalCAR: according to adjusted GDP pathCAR regulatory minimum

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• http://www.cnb.cz/cs/financni_stabilita/zatezove_testy/

• ZFS 2009/2010• Verifikace zátěžových testů jako součást pokročilého rámce zátěžového

testování • Procykličnost finančního systému a simulace „feedback“ efektu

• ZFS 2006,• Vývoj kreditního rizika a zátěžové testování bankovního sektoru v ČR

References

Testing resilience of the financial system

Documents

Transcript of Testing resilience of the financial system