Stress Testing & Scenario Analysis
Contributing to greater financial resilience
Securities and Investment Institute
Risk Forum
London, 11 December 2008
Risk & Capital Markets Advisory
Welcome and Introductions
Wolfgang Schwerdt
Director
Bancstreet Capital Partners Ltd
formerly Senior Economist
at the European Central Bank
Pekka Luoma
Chairman
Bancstreet Capital Partners Ltd
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Evolution of Risk Management
In the last three decades Risk Management has evolved
rapidly from dark art to mechanised industry.
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Evolution of Risk Management
 Move from static and subjective approaches to dynamic statistical
models.
 VAR, ETL and related approaches are now in near universal usage
and enshrined in regulations.
 Quantitative risk models have become technically feasible for almost
all aspects of risk including market-/ credit- and even certain aspects
of liquidity and operational risk.
 Risk information has become more transparent, available and
accessible.
 Risk Management has transformed from a “Dark Art” to a
Mechanized Industry.
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Why do we need Stress Testing ?
 Risk Management means
managing exceptions.
 Much of risk management
practice is concerned with
common and expected
exceptions.
 Stress testing deals with
uncommon, unexpected
exceptions that can have large
or devastating consequences.
The higher the stakes the greater the
importance of effective stress testing.
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Why Stress Testing ? A bit more detail...
 Historical statistical models like VAR/ETL
are simplifications and cannot capture
aspects of risk not directly observable in
past experience.
Expected Risk
measurable
“Un-Expected” Risk
Using VAR/ETL
remaining
Residual Risk
visualised through
Stress Testing
 VaR / ETL measures cannot capture the
effect of truly extreme, sudden & dramatic
changes.
 These frequently lead to the breakdown of
statistical relationships, like correlations, on
which risk measures are based.
 Stress testing tries to evaluate these
aspects, not covered by measurements of
expected risk and measurable, “UnExpected” Risk.
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Evolution of Stress Testing
From Sensitivities to Historical & Hypothetical Scenarios
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Stress Testing today…
 Stress testing today is performed through measuring VAR or ETL for a
simulated future using selected historical and/or hypothetical scenarios.
 This simulation based approach is helped by the advances in computer hard
and software, significant progress in Monte-Carlo and quasi random
simulation approaches.
 This type of risk analysis is very different from the measurement of historical
volatility or of a stock beta. It places enormous emphasis on skilful and
informed judgement.
 However, many organisations have difficulties injecting the necessary level
of skilful and informed judgement because:
 The convenience of historical data over appropriate hypothetical
scenarios.
 Difficulties of getting senior executives directly involved.
 Difficulties of overcoming the entrenched fragmentation at a systems and
organisational level.
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Stress Testing at major financial institutions
How safe are decisions
made on the basis of
common stress testing
practices ?
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Stress Testing at major financial institutions
 Consider the following points from the 2005 BIS survey:
 “Property price [hypothetical] scenarios [were] generally confined to markets
experiencing [a boom or which had big historical fluctuations].”
 “At a very basic level [...] stress testing is often undertaken in very different areas
of a firm, making internal consistency across integrated scenarios difficult.”
 “Treatment of market liquidity was identified as a key issue in the 2000 survey.
Only limited progress has been made since then.”
 “An unanticipated exit of a major market player is treated as one of the risk
sources [for] market turbulence. Not many institutions are running this type of
scenario.”
Quoted from a report by the Bank for International Settlements entitled “Stress Testing at Major Institutions:
Survey Results and Practice”, published in January 2005. (Pages 11-14)
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Is current Stress Testing practice in good health?
???
 Was poor Stress Testing & Scenario Analysis a cause for the credit crunch?
 Could more effective Stress Testing & Scenario Modelling have prevented
the credit crunch?
 Is current practice in Stress Testing & Scenario Analysis providing the
robust input into business practice and top level executive decisions it
should provide ?
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What happens in a true crisis?
Market participants have to survive in a world where
everything is upside down.
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What happens
Happens in
in aa true
Truecrisis?
Crisis ?
What
 Events cause a sudden move of prices, volatilities and correlations
to a spectrum well outside the normal range of observations.
 Correlations change, break down or even get reversed.
 Disappearing liquidity may make correlations irrelevant.
 Stable patterns may take a long time to re-establish themselves.
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What solutions are there today?
... And are they the on-coming train at the end of the
tunnel; or a new dawn, after these dark days?
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Integrating Stress Testing at business level
What tools do we need as a prerequisite to better stress
testing and scenario modelling ?
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The Stress Testing Space
Credit (counterparty) risk
Funding liquidity
Scenario analysis
Traded portfolio risk
Sensitivity analysis
Risk Factors
Methodology
Operational risk
Application
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Space Dimension 1: Risk Factors
 External factors the changes of which affect the firm’s balance sheet.
 Assets side:
 Traded portfolio risk: Interest rate, Equity, FX, Commodity and Credit
Derivatives
 Credit (counterparty) risk
 Liabilities side:
 Funding liquidity stress scenarios: changes in client behaviour, own
rating, funding costs and collateral requirements.
 Historical scenarios: LCTM & Asian crises.
 Operational Risk: not often tested due to data problems.
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Case 3:Counterparty Risk - Simulating the joint effects of market
movements and credit events in Trading Book
Alpha Testing as a Basle II short-cut
Credit
Risk
Market
Risk
Liquidity
Risk
 Alpha testing is a short-cut developed in the Basel II "cook book" to measure
the interaction of market risk and credit events.
 A global bank has calculated the ‘alpha’ coefficient for a trading book with

5,000 counterparties

100,000 long-term derivative contracts

Monthly time steps in simulation
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Space Dimension 1: Risk Factors
 External factors the changes of which affect the firm’s balance sheet.
 Assets side:
 Traded portfolio risk: Interest rate, Equity, FX, Commodity and Credit
Derivatives
 Credit (counterparty) risk
 Liabilities side:
 Funding liquidity stress scenarios: changes in client behaviour, own
rating, funding costs and collateral requirements.
 Historical scenarios: LTCM & Asian crises.
 Operational Risk: not often tested due to data problems.
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The Asian Contagion – A Stress Test Example
Asian contagion in financial markets
Trading Market Risk
Liquidity Risk
Trading Credit Risk
Asian Currencies
Declined
Market Liquidity
Dried Up
Credit Spreads
Widened
Equities Fell
Enterprise Liquidity
Dried Up
Credit Quality
Declined
Interest Rates
Unstable
Financial System
Under Stress
Defaults Increased
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Credit Risk – Stress Testing
. . .To produce perturbations of
combined Distributions of
simulated future credit losses
Credit risk factors
May be shocked . . .
Exposure
Defaults
PROBABILITY
Shocks
0
Recoveries
t0
tN
Correlations to other factors
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Space Dimension 1: Risk Factors
 External factors the changes of which affect the firm’s balance sheet.
 Assets side:
 Traded portfolio risk: Interest rate, Equity, FX, Commodity and Credit
Derivatives
 Credit (counterparty) risk
 Liabilities side:
 Funding liquidity stress scenarios: changes in client behaviour, own
rating, funding costs and collateral requirements.
 Historical scenarios: LTCM & Asian crises.
 Operational Risk: not often tested due to data problems.
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Operational Risk: not often tested due to data problems
- Frequency of events (should include “near-misses”)
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Operational Risk: not often tested due to data problems
- Severity, measured in money terms
Please compare
the shape of the rel.
frequency.
distribution with the
one on next slide
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Different types of operational risks
Single loss <
$1m
Quality assurance
errors
Larger
‘blunders’
Traditional controlled self
assessment tools
Material risk
concentrations
Not recognised by
managers as the problem
has been corrected
Structural operational risk
breakdowns outside
managers’ experience
Measure using actual Use extreme value theory
and Monte Carlo
losses
simulation
Evaluate exposure to
processes, estimate
probability of main causes
Within the control of
business managers
Absorbed by
normal
operating
costs
Possible
candidates for
insurance
Value
Sufficient
‘buffer’ capital
at group level
Material Risk Concentrations
 Relevance of scenarios will require senior executive level input and
involvement.
 Proritisation requires an assessment of sources of risk at a strategic
level, some not part of the experience of line management.
 Drill beneath risks that are observable in in-house and syndicated,
industry-wide incident databases.
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Operational Risk: not often tested due to data problems
- Exposure (any confidence level can freely be chosen)
Kurtosis
and skewness
statistics would
have told you
about the fat
tail even
without looking
at the graph
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Why does our headline still hold true…
… that Operational Risk often is not tested due to data problems?
Adapted from Janet Rogers,
Federal Reserve Bank of NY
Qualitative Approach - Judgemental
Develop
Operational
Risk
Function
Self
Assessment
&
Action
Plans
Develop
Policies
& Control
Framework
Define
KRI’s &
Escalation
Triggers
Quantitative Approach - Optimising
Develop
Feedback
& Improve
Performance
Allocate
Risk
Capital
Track &
Measure
Losses
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Measurable
Performance
Develop
OpRisk
Measurement
Model
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Space Dimension 2: Application
 Risk factor sensitivity
 Evaluate effect of extreme market movements
 Counterparty risk assessment
 Verify economic capital allocation
 Business risk evaluation
 Communication tool
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Space Dimension 3: Methodology
 3 (4) approaches, depending on whether one counts historical
simulation (3.) and full Monte Carlo (4.) as one or two separate
 Sensitivity analysis (1.)
 Source of shock not identified, instantaneous risk parameter move.
 Scenario analysis (2.)
 Single scenario identification; then “deterministic” simulation used
 Parameter calibration
 Historical: maximum loss events “limited by history”, once-in-a-thousand
years events occur every decade.
 Hypothetical: use statistical methods to work out Worst Case risk factor
values that are still plausible (e.g. by principal component analysis) even
when risk factors go “out-of-range”.
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Covering the entire business
 Scenarios that are generated with different assumptions and
approaches cannot safely be aggregated to help make decisions for
a whole business or portfolio.
 The set of scenarios should be carefully adapted to the business as a whole.
 The approaches and assumptions must be formulated in such a way
that they can be made to apply to the business as a whole.
 Tools to automatically detect which risk factors are operative, and the “quality
check” data on those risk factor inputs
 A suitable infrastructure and process is needed to make the
combining of external data (risk factors) and internal data
(exposures)
 cost effective and
 reliable and timely
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Integration at business level takes...
 A solution built around a path Monte Carlo simulation capability that is
 linearly scalable to handle any size of institution and any horizon for analysis, still
using a time step that corresponds to market or macroeconomic adjustments paths.
 Has an extensive pricing library, with an in-built capability to handle structured
products, too.
 Can handle thousands of risk factors so that both risk analyses and performance
attribution analysis can be made granular enough to incorporate all important
“decision levers”.
 And: has a standardised way of recording the financial characteristics
of all instruments for quick and easy integration (from existing or newly
introduced base systems).
 And: supports formulating different scenarios and incrementing them,
with a capability to easily analyse and compare the results.
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Questions?
for further information please contact:
Wolfgang Schwerdt
Email: ws@bancstreet.com
Pekka Luoma
Email: pekka.luoma@bancstreet.com
www.bancstreet.com
Bancstreet Capital Partners Ltd
Bancstreet House
21 Albert Road
Hounslow, Middlesex
United Kingdom
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