RISK SEMINAR SESSION 2 – STRESS TESTING AND LOSS FORECASTING · management decisions (on a...

© Oliver Wyman | LON-FSP03201-035

RISK SEMINARSESSION 2 – STRESS TESTING AND LOSS FORECASTINGNOVEMBER 2012

Stockholm, KTH

Contents

1. Stress testing – overview

2. The stress testing process

3. Macro models

Case study: Loss forecasting engagement

Stress testing – OverviewSection 1

33© Oliver Wyman | LON-FSP03201-035

Stress testing has become an increasingly important tool in risk management in terms of understanding and communicating risks, and planning for losses

Roles of stress testing in risk management

Source: Bank of International Settlements survey

• A stress testing framework improves understanding of risks as it– Forces the institution to conduct a

comprehensive review of it risk exposures

– Identifies the key drivers of risk(and losses)

– Usually does not rely on overly complex formulas to explain the losses

• The framework also helps in communication of risks as it– Prioritises the risk scenarios by impact

and likelihood– Focuses senior management attention

on the most important risks

• To be useful, the stress testing exercise should be transparent and intuitive –not a “black box”

100%95,3%

60,5%

48,8%

18,6%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

To communicate the bank’s risk profile to senior management

To understand the nature of the bank’srisk profile

To set limits forrisk-taking

To conductcontingency

planning

To allocatecapital

% o

f res

pond

ents

Stress testing needs to be communicated to senior management in a way thatcan be easily and intuitively understood


There are different types of stress testing

Hypothetical scenarios• More relevant to portfolio and current

market environment than historical scenarios

• Labour intensive

• Involve more judgment– Usually created with input

from experts- Management- Business level- Macro-economic models

Sensitivity tests

• Defined by shift inunderlying variable

• Relatively easy to defineand implement– Often used at trading desk and

business line level

• Shifts in several variables have tobe used in order to “simulate”historical events

• Correct use of stressed correlations between risk types is crucial– Difficult to parameterise

• Get high level estimate of likelihood of each scenario

• Ensure coverage of all risk types that the bank faces

• Ensure coverage of major portfolios by dedicated elements in each stress scenario

Historical scenarios• Choice of different scenarios that

are most relevant for different partsof the portfolio

• Coverage at least of major risks inthe portfolio

Scenario tests

Black Monday Oct. 1987

Asian Crisis 1997

9/11 terrorist attacks

Hybrid scenarios

• Hypothetical scenarios that are based on historical scenarios– Adjusted historical scenarios– Price sensitivities are set using historical

events– Effects of events on market liquidity are set

using historical scenariosRating class

Expo

sure

0

5

10

15

20

25

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Trade-off between ease of understanding vs. realism is crucial


The outcome of a stress scenario is examined in terms of its impact on P&Land capitalisation

Bank ($MM) Effect of stress

Revenue

Total $XX,XXX

Expenses ($XX,XXX) Revenue assumed to be unaffected

Pre tax, provision profit $X,XXX

Less

Losses $X,XXX Long run EL figure replaced with predicted actual lossesin a crisis

Profit/(Loss) $X,XXX Profitability and returns reduced by difference betweenactual and expected losses

Capitalization

Tier 1 capital $XX,XXX Any negative profits will be subtracted from Tier 1 (and total) capitalization

Total capital $XX,XXX

Required capital $XX,XXX No change in capital required – ratings assumed to be stable over the cycle

Excess capital $X,XXX

Tier 1 ratio X%

Total ratio XX%

Return on actual cap XX%

Return on cap required XX%


Some examples of stress test scenarios

Type Stressed scenario DescriptionGeneralstresses

Worst case scenario downturn • What would happen given current rating distribution, credit policies etc. if a downturn similar to that of Thailand in 1997 was experienced domestically

Worst case scenario downturn –no recapitalization

• What would happen given current rating distribution, credit policies etc. if a downturn similar to that of Thailand in 1997was experienced domestically, lasting for three years without recapitalization

Specificstresses

Real estate collapse • Downturn of 33% in property prices

Sector crash – oil price rise • Impact of significant increase in oil prices

Stock market crash • Impact of a sharp fall in stock prices, leading to margin calls and foreclosure of major institutional andretail accounts

Financial sector liquidity and asset quality crisis

• Fallout from global asset quality and liquidity concerns on Chinese banking sector

Single or joint large group default(s) • Defaults of the bank’s largest group(s)– Both from an EL and exposure perspective


Regulators increasingly consider stress-testing a central part of a bank’s risk management – and one that has been inadequate historically

Scope of stress-testing exercise

• All risk types to be considered– Additional focus to put on

liquidity risk stress testing– Counterparty risk considered

in integrated fashion

• Effects across risk types tobe considered– Consistent approach– Consider contagion effects

between risk types

• Increased role for expert judgement– Use of purely statistical

models identified as weakness

Governance/use of results

• Senior managementinvolvement key– Deciding on scenarios– Signing off models and results

• Stress-testing results to be actionable– Result in concrete actions– Institutions to develop

contingent strategies

• Institutions to ensure capital planning capabilities

• Stress-testing results used in external communication

• Reverse stress-tests– Institutions to identify

scenarios that cause business to become unviable

• Forward-looking focus– Based on realistic threats– Not solely restricted to

replaying historical events

• Decoupling from confidence interval focus– “1 in 25” approach to defining

scenarios losing importance

Range of scenarios considered

Source: FSA Consultation paper CP08-24, BIS paper “Principles for sound stress testing practices and supervision”

Regulatory focus


However, stress and scenario testing is still highlighted by many as a gap in banks’ risk management framework

Need for stress-testing highlighted

• Industry difficulties attributed in part to misunderstanding the impact of “events”– Appropriate stresses not considered– Impact of stresses not well understood

• Stress-testing near the top of the agenda for regulators, rating agencies and analysts– Recent BIS and UK FSA publications – Broker reports/valuations

• Economic outlook particularly uncertain, with uncertainty under-scoring the need for stress testing insights– Characteristics of the current recession

unclear (deflation, hyper-inflation…?)– Post crisis regulatory and competitive

landscape unclear

Many institutions fall short of requirements

• Few executives regularly utilise stress/scenario results in decision making– Limited value realised from current capabilities

• Most institutions lack elements of analytical “technology”– Most have some siloed models (e.g. liquidity

stresses)…– …though few meaningfully model the impact of

scenarios– Many miss a holistic view of all risks/

products/businesses

• Substantial ambiguity about required or best practice– Regulatory requirements– Processes and what to do with the insights

provided


In best practice organisations stress testing serves both regulatory and business objectives

Regulatory driven objectives Business objectives

• Ensuring institution understands the risks it is holding –for this it is not important to have perfect modelling capabilities but a holistic understanding of the causal chains

• Ensuring the institution is adequately capitalised today, in future under basic and in stressed conditions

• Ensuring Risk Appetite is linked into Capital Adequacy calculations and monitored

• Ensuring EC methodology is appropriately challenged

• Ensuring losses are appropriately forecasted

• Ensuring adequate support is provided for management decisions (on a regular and ad-hoc basis)

• Understand the overall risk profile of the business and communicate it to Senior management

• Set limits for risk-taking, i.e. redistributing risk-taking in order to decrease vulnerability

• Ensuring annual business plans reflect not only a base case but also other potential scenarios

• Base strategic investment decisions on a multitude of planning scenarios

• Provide input into portfolio steering activities

• Perform loss forecasting and assess impacts on business

• Conduct contingency planning and use as early warning signals

• Support external communication to investors and other stakeholders

Stress testing is not a standalone risk management activitybut always works in conjunction with other activities

From a business perspective stress testing can support decision making by delivering better information

The stress testing processSection 2


To create real impact from stress testing while meeting compliance goals, processes and analytics must be addressed simultaneously

Use Ensuring that results drive

business decisions

AnalyticsModelling the impact

of stresses

Scenario generation Defining and agreeing stresses

• Identify relevant risks and opportunities to the business/deal – Understand sensitivities and

market situation– Ensure full input and engagement

from the “coal face”

• Develop internally coherent “stories” that link together the issues

• Agree believable/credible scenario sets that challenge conventional wisdom

• Develop strategies considering a range of possible scenarios– Position for upside, limit downside– Link into planning process

• Develop contingent strategies – Macro hedging– Early mover advantage

• Monitor situation and use scenarios for short term forecasting

• Link scenarios into risk appetite

• Assess impact of scenarios on business– Asset portfolios– New business margin

and volumes– Funding/capital impact

• Ensure stress-testing is flexible enough to model strategic choices– e.g. Impact of changing

credit policies

• Highlight key results/issues (e.g. Capital, earnings…)

Feedback loop


Best practice scenario generation is an iterative process, including a range of sources of scenarios that are aligned to business and economic uncertainties

External changes• Regulatory initiatives • Market/competitive changes

Market events • Key markets shut down• Volatility in specific areas

• Macro economic possibilities • Economic “shock” impacts

Economic scenarios

Internal sensitivities• Known concentrations,

issues and sensitivities• One off events

• Capital increase• Ban on short selling

• FX market halts• Gold market

• Deflation/hyper inflation• Currency collapse

• Default of largest name• Drop in real estate

market

• Scenarios taxonomy must cover relevant threats and opportunities – “Ad hoc” investigation of specific

current concerns– Constant issues (to allow through-time

comparison)– Confidence interval based (reg.

requirement)– Reverse stress tests – Etc.

• Scenario discovery should include feedback from regular processes (e.g. planning/ budgeting rounds, risk appetite setting etc..)

• Numerous stakeholders included (Group economics, Risk, Finance, Business leaders etc.)

• Scenarios reconsidered/re-designed after each round

Aim is not to predict the future. Instead to highlight a set of issues and facilitate

preparation for the unexpected

Scenario taxonomy and examples


Scenarios should be generated using a blend of historical and hypothetical scenarios: Kis to parameterize all relevant risk drivers

High level story

Expert overlay

Risk drivers dynamics

Complete scenario

Impact on the Bank

Historical and academic theory analysis to calculate interconnection between risk drivers

Example: House prices fall 40%, what happens to GDP, interest rates, unemployment, stock markets…

Draw a coherent and plausible story that would impact the activity of the Bank

Example: “House prices fall 40%”

Challenge historical relationships before scenarios are finalized. Incorporate

• Executives’ opinions on changing relationships

• Specific experts’ inputs

All relevant factors are detailed and quantified

Running the model the effects of the scenario on the Bank are quantified


First, typical risk drivers need to be identified

Equity price risk

Interest rate risk

FX risk

Real estate price risk

Credit Risk (PD, LGD)

Key risk drivers

Credit risk should be divided into credit risk (PD, LGD) and credit spread risk

Relevance of drivers to risk units

Credit spread risk

Life/non-life risks

Key risk driverMarket

risk – bankMarket

risk – insurance Credit risk

Equity

Interest rate

FX

Real estate

Credit

Credit spread


Subsequently, a short list of scenarios can be iteratively distilled on the basis of a long list which is derived from the risk drivers

• On the basis of the identified key risk drivers, a long list of scenarios can be derived

• This long list needs to be subsequently cut down to a sort list

• A first pass high level quantification of scenario impact helps to identify crucial scenarios

• In our experience, a short list of 5–7 scenarios is adequate and desirable

> 100 scenarios

~ 50 scenarios

~ 20 scenarios

Final objective:5–7 scenarios

Quantification and framework of risks

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Key risk drivers

16© Oliver Wyman | LON-FSP03201-035 16

The earnings and value impact of the final scenarios are then calculated by each risk unit

List of scenarios and risk driver impact

• Each scenario is characterised by one or several stressed to underlying risk drivers

• Stresses are defined as immediate (overnight) changes in a given risk driver, assuming that management has no time to intervene and mitigate the impact

• The earnings impact of a given stress is defined as the end of year change in reported IFRS earnings

• By economic impact, we mean the change in marked-to market net asset value

Comments

Scenario Driver Impact

1 Global demand slump/asset bubble

• Equity prices• Interest rates• Expected loss• Credit Spreads

• Drop 30%• Down by 30%• Up by 130%• Up 100%

2 Real estate crisis

• Real estate prices• Expected loss

• Drop 20% (residential)

• Drop 40% (comm.)• Up 20bps

(residential)

3 US crisis • US equity prices• US Interest rates• USD currencies

against €

• Drop by 30% • Up 60%• Drop by 35%

4 Emerging market crisis

• Emerging markets equity prices

• Emerging markets interest rates

• Emerging markets currencies against €

• Drop by 30% • Up 60%• Drop by 35%

5 € & USD yield curve flattening (single factor stress)

• Short term interest rates

• Five year rate to be applied to entire curve

Illustrative


• Internal– Alignment of terminology and approaches

for Pillar 1 and Pillar 2 stress testing• External

– Communication of scenario effects and management preparation to give confidence to investors, ratings agencies, and regulators

Communication

• Ensure that all relevant Pillar 1 and Pillar 2 regulatory requirements on stress testing are identified and addressed; leading to improved dialogue with regulators and decreased likelihood of punitive capital add-ons

– SARB– FSA– Etc.

Compliance

• Linking the strategic planning and budget process to considerations of “different states of the world” and how scenarios may evolve over time

• Inform M&A decisions• Formal established process to analyse the

implications of asset growth

Strategic planning

• Group estimation of the losses we are likely and able to bear in a macro-economic downturn, real estate crisis, etc.

• Understanding group-wide exposure to specific sectors and geographies (concentrations)

Understanding and steering of risk profile (risk appetite linkage)

Loss

Prob

abili

ty

• Analyse capital adequacy under scenarios• Choose appropriate capital targets and

allocate capital

Capital management

• Tie specific mitigating actions to macro-economic scenarios and increase management preparedness– Funding strategy changes– Cost cutting (e.g. staffing related)– Credit growth and recovery

strategy changes– Restructure positions and

hedge portfolios

Contingency planning

To provide a bedrock to risk management, stress-testing should be embedded across a range of management processes

Capital

Time

Buffer

Capital supplyStressed capitalCapital demand

Examples of use of stress/scenario testing in running a bank

Macro modelsSection 3


State-of-the-art proprietary credit risk models capture the impact of changes in global economic factors on the portfolio

Illustrative

Macro modelCorrelation calculations

and scenario simulations

What-if analysis(conditional loss analysis)• Estimation of future losses

• Economic impactstress tests

• Loss sensitivity analysis

Early warning systems

• Impact assessments of lagged factors

Portfolio management strategy assessments

• Hedging programs– Interest rate swaps– Index swaps

Macro factor historical indices

• Unemployment

• Interest rates

• Oil prices

Loss indices for internal portfolios

• Aviation

• Commercial paper

• CDOs

Loss distributions for portfolios

• Aviation

• Commercial paper

• CDOs


Macro-economic models are regression based models that take into account macro-economic factors to estimate portfolio level losses

• Macro-economic models estimate portfolio level losses in the future based on current state of the economy

• Such factors include SP500 index, unemployment, GDP, LTV ratios, disposable income, retail sales, loan balances, etc.

• These models may be used on a standalone basis to predict future losses at a portfolio level or be used together with application scorecards and behavioural models to adjust calculated expected loss levels point-in-time estimates

Example calculation

0.00%

0.05%

0.10%

0.15%

0.20%

0.25%

Jun-

88

Jun-

90

Jun-

92

Jun-

94

Jun-

96

Jun-

98

Jun-

00

Jun-

02

Jun-

04

Jun-

06

Cha

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ate

Actual Predicted

Option 2Variable ParametersStandard ErrorT Stat P-Value R SquaredIntercept -13.623 0.749 -18.183 0.000 86%S&P 500 Index y/y % -0.008 0.001 -5.295 0.000Retail Sales y/y% -0.094 0.014 -6.912 0.000Mortgage as % of Disposable Income 0.717 0.074 9.712 0.000LTV Ratio y/y% 0.043 0.015 2.901 0.005


• Study statistical relationship between macroeconomic variables and NPLs

• Worksteps include data collection, creation of a long list, single factor analysis, definition of a long list, multi-factor analysis, creation of the base model, agreement on final model

• Analysis could be performed across a panel of countries to identify “fundamental” relationships

• Results are checked against historical observations from other crises, recent developments, Oliver Wyman benchmarks, expert opinion, etc.

• Agree model to be used for translating macro variables into borrower and portfolio impact e.g. Merton, Markov chains, direct stressing of model factors, top-down macro linkages, etc.

• Determine approach for accounting for severity of macro shock, borrower correlation with the market, rating model cyclicality, multi-year scenario evolution

• Calculate stressed recoveries and LGDs

• Translate stressed NPLs and recoveries/LGDs into provisioning charges by accounting for management intervention

Sense-check results

The development of macro-economic models typically follows a two step process

Determine impact at client level Develop macro relationships1 2

3


Number of insolvencies vs. GDP growth

-3%

-2%

-1%

0%

1%

2%

3%

4%

5%

6%

-3 000

-2 000

-1 000

0

1 000

2 000

3 000

4 000

5 000

6 000

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

Year

Total insolvencies Real GDP growth

Rec

essi

on

Rec

essi

on

Rec

essi

on

Macro economic stress test engines for credit losses – Client example

Business Unit FactorsExplanatory Power

1. Mortgages • Two year change in interest rates

• Six month unemployment growth

• Six month house price growth

• 84%

2. Personal Lending

• Annual Real GDP growth • Annual house price

growth • Annual unemployment

growth

• 65%

3. Credit Cards • Annual Real GDP growth • Six month unemployment

growth • Six Month change in

interest rates

• 53%

The macro relationships can be established based on statistical analysis, However this must be complemented by historical observations from other crises, recent developments, Oliver Wyman benchmarks, expert opinion, etc.

Num

ber o

f ins

olve

ncie

s Real G

DP grow

th rate

Client example

1

Develop macro relationships


The macro model should be based on fundamental relationships which are proven to work across a number of countries and time periods Example: Macro modelling across a panel of countries for a Russian client

• Total NPLs• NPLs of corporate loans• NPLs of commercial real estate loans• NPLs of consumer loans• NPLs of residential real estate• NPLs of leasing• Provisions • Write-offs• Bankruptcy /insolvency data

• Russia• Brazil

• China• India

• Italy• Poland

• S. Africa• Turkey

• UK• US

• Bond index• Equity index• Average exchange rate• Foreign direct investment• Nominal GDP growth• Real GDP growth• Goods imports • Gross financial instruments• Average inflation• End-of-period inflation• Interbank rate• Margin on local interbank rate over T-bills• Eurobond index• Nominal GDP• Personal disposable income• Private consumption• Property index• Real GDP • Total exports • Unemployment• Unemployment rate

• Lag• Lag

• Lag• Lag• Lag • Lag• Lag

• Lag• Lag• Lag • Lag• Lag• Lag• Lag • Lag

X

…for the following countries

…on the following X variablesWe regressed the following Y variables…

Develop macro relationships

1


Developing the optimal macro relationship requires an iterative approachTypical steps include: creation of a long list, single factor analysis, definition of a long list, multi-factor analysis, creation of the base model, agreement on the final model

Short list

Variable Correl1. Combined interest rate factor (r squared weighted average of short, medium and long term rates)

+86%

2. Prime rate (weighted average of lag 0 and lag 1) +77%

3. 0–3 year government rate (weighted average of

lag 0 and lag 1)

+82%

4. 10+ year government rate (weighted average of

lag 0 and lag 1)

+84%

5. Home price index (log diff, weighted average of lag 0 and lag 1)

+80%

6. Household debt to income ratio (lag 1) +72%

7. MSCI Index in USD (1994 – Present only, log difference, weighted average of lag 0 and lag 1)

-65%

8. Real GDP (log difference, weighted average of lag 0 and lag 1)

-64%

9. Gold price in USD level (weighted average of lag 0 and lag 1)

-62%

10. Gold price in USD (log difference, weighted average of lag 0 and lag 1)

-57%

11. FX: Local currency to Euro (weighted average of lag 0 and lag 1)

-25%

12. Equity index (log difference, weighted average of lag 0 and lag 1)

-15%

13. CPIX (percentage change, weighted average of lag 0 and lag 1)

-13%

Variable Weight1. Combined interest rate factor +39%

2. Home Price Index -35%

3. Household Debt to Income +17%

4. Real GDP -16%

Selection criteria

Base model

Client exampleDevelop macro relationships

1

• Intuitive signs

• Model fit

• Balance of factor weights

• Diversity of factors

• Five factor limit


• Captures how much of the risk is explained by the overall economy vs. idiosyncratic events

• Depends on the borrower’s industry/segment, borrower size

• Assesses how severe the macro shocks are compared to history and expected future evolution

• Determines the overall macro shock using the regression equation

Severity of macro shock

Borrower correlation with the marketNAV

DDnormal

Normal economy

Severe recession

Normal PD Stressed PD

DDstressed

In the Merton framework, a company is considered to be in default if the value of its assets falls under the value

of its liabilities (negative net asset value – NAV)

• Determines the level of cyclicality of the model depending on the segment, structure, etc.

• Uses model cyclicality to translate between (hybrid) PDs and (PIT) default rates

Rating model cyclicality

• The analysis should account for the starting macro environment as well as the expected portfolio evolution over time

Multi-year scenario evolution

A

The client level impact can be determined via a number of approaches e.g. Meton model, Markov chains, direct stressing of model factors, etc.

B

D

C

Determine impact at client level

2


4,7%3,9% 4,0%

6,8%

7,9% 8,1%

9,9% 10,3%

0%

2%

4%

6%

8%

10%

12%

Macro stresstesting

External dataapproach

Internal dataapproach

Macro stresstesting



Macro stresstesting



No relevant historical event for the bank

Mild recession Severe recession Extreme recession

GDP 0% GDP -5% GDP -10%

As a sense-check, the results from the macro model can be validated against external and internal loss experience

Example: Credit stress testing for an Asian bank

Sense-check results Client example

3PD


Household debt/income

Real GDP growth

Property price index

Equity Index

Interest rates

Factors

Client example: credit risk macro model developed by Oliver Wyman in Emerging Markets

Model performance – 2006 and thereafter is out of sample

0

0.5

1

1.5

2

2.5

3

3.5

4

4.519

8019

8119

8219

8319

8419

8519

8619

8719

8819

8919

9019

9119

9219

9319

9419

9519

9619

9719

9819

9920

0020

0120

0220

0320

0420

0520

0620

0720

08

Actual

Prediction

In-sample performance

Out-of-sample peformance

Strong model performance out-of-sample

Loss forecasting engagementCase study


• Oliver Wyman was commissioned to develop an impairment forecasting methodology for one of the world’s largest banks, link the stress testing process to strategic decision making and develop an appropriate governance structure

• The scope of the project was very broad; this case description only explains how the impairment forecasting process was developed for the bank’s securitisation portfolio using four high-level steps


Oliver Wyman supported a major international bank with development of loss forecasting capabilities

Secondary vector generation

Default rate projection

Macro–economic modelling1 2 3 Impairments

forecasting4• Forecast of default rates for

asset pools backing each deal under a base, stress and extreme stress scenario

• Forecast is based on macro–ecnomic environment projection and historic performance of the individual asset pool backing each bond

• Generation of secondary vectors (prepayment rates, cure rates, recovery rates) for each exposure‘s asset pool, under each scenario over time

• Calculation of impairment to each bond, based on losses booked on bonds over their lifetime

• Identification of relationshisp between key macro–economic factor and default rates


Default rates under different scenarios were projected using statistical relationships between credit risk parameters and macroeconomic variables

• Derive dependent variable series from various sources (e.g. observed default rates)

• Select appropriate functional form for dependent factor, typically Value

• Analyse each independent factor’s ability to predict the dependent factor, under different functional forms– Conduct regression

analysis– Create shortlist

of independent factors

– Assess stability over time

• Regress the dependent variable against a combination of predictive independent factors

• Analyse the sign and magnitude of the coefficients, the p-values and R-squared measures

• Determine the final model

• Create different functional forms, gaps and lags for each of the four agreed independent macro-economic factors, including– Value– Difference– Growth rate (%)

• Create a correlation table for the short list of macro-economic factors

• Use the results from the single factor analysis in combination with correlation analysis and judgement to determine potential models for multi-factor analysis

• Generate forecasts using forecasts for the macro-economic factors under agreed scenarios

• Apply smoothing, conservatism overlay

• Regression analysis was employed to reveal a statistical relationship between the probability of default (dependent variable) and macroeconomic variables (independent variables) based on historical relationships– For example, how changes in unemployment and interest rates may affect default rates on consumer

loans in a country


Select dependent variable

Create functional forms for independent variables

Conduct single factor analysis

Analyse correlations

Conduct multi factor analysis

Generate forecasts


Five independent variables were chosen… …but only three were used for each model

• The following four macro-economic factors were the used as independent variables in the regression models‒ House prices‒ Unemployment‒ Interest rate‒ GDP‒ Commercial property index

• Furthermore, each independent variable could be used in one of the following three forms‒ Absolute level ‒ Year-on-year (YoY) percentage change ‒ Delta (Year-on-year absolute change)

• Where possible, the longest time series were used to fully reflect the through the cycle average and volatility. Therefore the time period used in the regression depended mostly on the length of the dependent variable time series

• The number of independent variables was limited to three

‒ Increasing the number of independent variables would normally increase the explanation powers (i.e. regression r-squared) of the independent variables

‒ However, it would also introduce the problem of multicollinearity whereby the independent variables themselves are strongly correlated with each other, rendering the regression model less powerful

• The lag is an important feature of the macroeconomic regression analysis

‒ For example, when unemployment rises in month m, residential mortgage default rates do not rise immediately as people usually have some savings and/or will do everything they can to save the house before falling into default

‒ Therefore the rise in default rate will usually follow at some quarters later and there is a lag between the rise in unemployment and the rise in default rate


Regression models were developed using historical data from five macro-economic factors


Default rates were forecasted based on the outputs from the macro-economic models and a number of adjustments

• Once macro-regression models has been developed, future default rates were projected based on assumptions on the future performance of the economy– Three scenarios of varying severity were produced, containing different

macro-economic assumptions– Secondary vectors (prepayment rates, cure rates, recovery rates) were generated based

on historical data– Default rates were adjusted based on the outputs from secondary vectors– To further refine outputs, “vintage curves” were overlaid

- Any static loan pool typically exhibits a “vintage effect” which effect states that from point of origination, as a loan pool ages, it tends to exhibit a standard profile – default rates start low, rise over some period to a peak and then tail off or flatten out

- For revolving pools of loans, loan pool age varies over time in a non-predictable way so vintage effects in revolving loan pools were not modelled


Illustration of the asset pool engine for a performing loan

• EUR1MM exposure is performing at t=0

Quarters0 1 2 3 4 5 6 … 19

Performing loan

Defaulted balance

Cure

Bal. tosale

Doubtful

Loss realisation

EUR1,000 K

EUR10 K

EUR5.5K

EUR4.5K

EUR4.5K

EUR4.5K + accruals

• Quarterly default rate of, for example, 1% implies EUR10 K would be classed as defaulted in 1Q

• 2Q time-to-cure and 55% cure rate means EUR5.5 K would be returned to the performing portfolio at 3Q

• The residual balance (EUR4.5 K) would be classed as balances that should ultimately roll to sale

• The balance is moved to doubtful status at 18 months in arrears

• At the 19th quarter, the loss will be realised– Recovery: min(0.975 * (4.5 K+int

accrued), indexed property value at Q19*65%)

– Loss: (4.5 K + accrued int) less recovered amount

Note: Above excludes prepayment and amortisation of exposure, for simplicity


RISK SEMINAR SESSION 2 – STRESS TESTING AND LOSS FORECASTING · management decisions (on a...

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