Risk Intelligence: The 21st Century Frontier of Market Efficiency
CRITICAL SUCCESS FACTORS IN IMA IMPLEMENTATION
PHILIPPE CARREL
Mumbai, July 21st 2010
THE ROAD TO SYSTEMATIC RISKS
Number of crisis over time
appears to be rising
370 years of cyclical crises always
resulted from decoupling the perception
of current risk versus future value
REGULATIONS AIM AT DE-RISKING VITAL
ACTIVITIES
Idiosyncratic risk stabilised through adjusted through
risk adjusted capital reserves
CAPITAL
Credit Risk Weighted Assets + [12.5 x (Mkt Cap Charge + Ops Cap Charge)]
Systematic risks through countercyclical prudential
supervisory measures.
BCBS 164 on strengthening resilience contains proposals for capital
buffers to contain leverage and exposure
IDIOSYNCRATIC RISK MANAGEMENT IS TO
BALANCE THE CREATION OF VALUE WITH
EXPOSURE TO RISK FACTORS
Corporate Strategy
Shareholders
Funding Strategy
Debt, Bond holders
Risk Appetite
• Exposure
• Sensitivity
• Maximum Loss
Risk is a measure of sensitivity to factors of
exposure under scenarios
Managing risk is to align the firm’s exposure to the
risk factors with its appetite for it
MANAGED IN SILOS, RISK IS
NECESSARILY AGGREGATED BY MODELS
Banking Books
Collateral
Trading Books
Markets
Portfolios
Economy
Growth
Country
Operations
Market Risks (ALM)
Credit Risks (EL=PDx[1-LGD])
Market Risks (Haircut)
Credit Risks (EAD)
Market Risks (VaR)
Credit Risks (CVaR, PFE)
Operational Risks (PE x LGE) or OpVaR
Net
RWA
Aggregated Loss
Distribution
EL
VaR
UL(i, j , a) =VaR( a) - EL(i, j )
confidence level
A FOCUS ON LOW IMPACT HIGH FREQUENCY
EVENTS REDUCES CAPITAL CHARGE…
Probability
of Loss
Event
Expected
Losses
VaR
Stressed VaR
Catastrophic
Scenario
Repetitive
tail events
Scope of Basle II
Expected losses
Outside the scope
of Basle II
Loss Impact
Interval of
confidence
Outside the
scope of B II
But increases exposure to tail risks…
..and to system externalities.
CREATE A CULTURE OF RISK
MANAGEMENT – KEEP IT ALIVE
Restore the balance Capital Efficiency / Risk to align
Corporate Governance and Risk Appetite
Support Regulatory Compliance with information on
market behaviour in addition to statistical analysis
Risk Intelligence
No financial instrument is inherently risky
Valuation and aggregation methodology (covariance) depend on the
nature of tail events
Crashes follow booms, but the future is not like the past
RECONNECT SENSES TO CREATE
A DNA BACKBONE
Banking Books
Risk Factors
Markets
Portfolios
Economy
Growth
Country
Operations
Markets
Portfolios
Economy
Growth
Country
Operations
Valuations
Trading Books
Markets
Portfolios
Economy
Growth
Country
Operations
Counterparties
RECONNECT THE BRAINS WITH THE
NERVOUS SYSTEM
Portfolio view of firmwide risks, limits and triggers
Business Line Risk Mgr
Net Exposure
Sensitivity
Max Loss
Product
Risk Mgr
Regional
Risk Mgr
Portfolio Limits
Sensitivity Limits
Concentration Limits
P/L Limits
CAPITAL & LIQUIDITY SHOULD BE DRIVEN
BY RISK INTELLIGENCE NOT ONLY RWA
CREATING A RISK INTELLIGENT GOVERNANCE AND COMPLIANCE FRAMEWORK
• Multiple vendor feed
• Internal pricing feed
7
2
Cross-silo exposure from:
• business lines
• products
Portfolio
• regions
1 Intelligence
• Exposure
• Sensitivity
• Max Loss
Market
Intelligence
RISK
FACTORS
4
Scenario
5
Simulations
Global Risk Infrastructure Framework
Reverse
Stress Test
8
• Gaps
• Concentrations Liquidity
• Contingencies
Intelligent
Data
3
• Reference data
• Counterparty data
• Ratings
Risk
Intelligence
Risk
5
6
Enterprise Risk Management Monitor
Capital &
Liquidity
Strategy
CREATING RISK INTELLIGENCE
GOVERNANCE
DRIVEN
COMPLIANCE
DRIVEN
• Enterprise-wide aggregation (by risk factor)
• Sensitivity analysis (portfolio and entity level)
• Stress testing
• Effective counterparty exposure
• Expected Positive Exposure (EPEs)
• Credit Value Adjustments (CVAs)
• Liquidity Risk Management
• Stress test ALM & gap analyses
• Counterparty driven gap analyses
• Collateral liquidity
• Valuations of OBS exposure
• Limit & Collateral Management
• Net counterparty exposure
• Risk concentration and sensitivity limits
• Leverage ratios and OBS
THE FALLACY OF MODERN FINANCE THEORY
Modern finance theory leads to
• Measuring expected return as a function of volatility (CAPM)
• Diversifying risks through expectations of low covariance
• Expressing tail event probabilities as a frequency of occurrence
Rs = i +  (Rm-i) + a
Rs = Expected return on the security
i = risk-free return
Rm= Expected return on the market
 = Cov(s,m)
Var(m)
Expected
return
Efficient frontier
a
Mkt index
b
i
0
1

The act of (collectively) observing an area of financial safety makes it risky
A. Persaud. Dec 2002
COVARIANCE RELIES ON INVESTORS’
BEHAVIOUR NOT ON HISTORICAL DATA
Correlation Matrix
Correlation
Values Absolute
Interval Weekly
Color Codes Threshold1:
List Setup
Calculate
Currency
GBP=
EUR=
INR=
JPY=
CNY=
XAU=
CLc1
0.5
Hide If
GBP=
1.0000
0.9050
0.4909
0.1027
0.2596
-0.7041
-0.2368
EUR=
0.9050
1.0000
0.3198
-0.0021
0.3808
-0.6835
-0.0901
INR=
0.4909
0.3198
1.0000
0.0066
0.0667
-0.6296
-0.8099
JPY=
0.1027
-0.0021
0.0066
1.0000
0.4666
-0.3922
0.1233
CNY=
0.2596
0.3808
0.0667
0.4666
1.0000
-0.4171
-0.0595
StartDate 02 Aug09
Threshold2:
Correlation
XAU=
-0.7041
-0.6835
-0.6296
-0.3922
-0.4171
1.0000
0.3961
0
EndDate
Threshold3:
is above
CLc1
-0.2368
-0.0901
-0.8099
0.1233
-0.0595
0.3961
1.0000
Variables are wrongly assumed to be independent
18 Jul10
-0.5
0.5
SPIRIT OF BASLE III
(BCBS 164 on Strengthening Resilience)
Quality and consistency of capital base
T1 Equity only
T2 5 year minimum maturity , hybrids phased out
T3 abolished
Enhanced risk coverage
Stressed VaR (includes periods of stress)
Credit Value Adjustment (CVA) to represent counterparty risk in market exposure
Push on centralised clearing counterparties
Wrong-Way risk
Leverage ratio
Ratio added to Pillar1 calculated with credit conversion factors
Focus on off-balance sheet items
Counter-cyclical measures
Probability of Default (PD) and Exposure At Default (EAD) computed over long term
Expected Loss (EL) to replace IAS39
Capital buffers to limit excess credit and leverage
Global Liquidity Standard (BCBS 165)
CREATING A RISK INTELLIGENT INDUSTRY
GOVERNANCE
DRIVEN
COMPLIANCE
DRIVEN
Liquidity
Dynamic gap analysis under scenarios
Concentrations on funding sources
Stress tests of exposure and collateral
Counterparty Risks
Concentrations and leverage
Collateral and margin management (reflect concentrations)
Market Risks
Concentrations, root risk and indirect exposure
Credit and liquidity risk priced in market risk
Mark-to-volitilty, mark-to-liquidity
Volatility and correlations
Potential reverse impact of volatility and concentrations on
correlations correlation and market depth
ENHANCE TRANSPARENCY
Market
Duration class
Coupon
Participation
Principal Protection
Convertibility
Path dependence
Short/Medium/Long/Extended
Fixed/Variable/Minimum guaranteed
Multiple/Full/Partial/Variable
None/Partial/Full
Auto/Dynamic/Periodic/Synthetic
Callable/Auto/Barriers
Legal
Supervisory body
Regulatory Region
Bank/Securities/Exchange/Others
EU/US/Other OECD/Others
• Attach risk-ratings to ALL instruments including OTC and funds
• Rate financial risks, volatility, liquidity, transparency
• Adapt valuation frequency to risk ratings (Mark-to-Risk)
MONITOR BEHAVIOURS
Price
Risk Class
A: Depth/Liquidity
B: Quotation frequency
C Typical Slippage
D: Spread/Price Volatility
E: News/Data Availability
Rating
0 to 5
0 to 5
0 to 5
0 to 5
0 to 5
• Attach risk-ratings to ALL instruments including OTC and funds
• Rate financial risks, volatility, liquidity, transparency
• Adapt valuation frequency to risk ratings (Mark-to-Risk)
RISK & LIQUIDITY CONCENTRATION
BENCHMARKS
Banks contribute foreign exchange claims in US$m by
1-Currency
2-Instrument type (fxswap, loan type, asset, liability)
3-Tenor (time bucket)
4-Volatility time bucket (if applicable)
5-Strike bucket (if applicable)
1
Regulators input scenario
1-Interest rates
2-Exchange rate
3-Volatility
4-Correlation
Term Structure of
Asset/Liabilities
by currency
2
RISK AGGREGATOR
o Aggregated views foreign exchange claims
by time bucket
o Gap analysis (Asset/Liability mismatch)
o Sensitivity analysis (under scenarios)
o Volatility concentration matrices
o Strike/Barriers concentration matrices
3
o Central bank gets view of
potential bubbles
o Regulators can anticipate on
funding issues per currency and
instrument
o Aggregated risk view in base
currency
o Banks can benchmark their
funding risk against industry view.
AGGREGATED TERM STRUCTURE OF
MISMATCHES IN FOREIGN CURRENCIES
Banks contribute foreign exchange claims in US$m by
1-Currency
2-Instrument type (fxswap, loan type, asset, liability)
3-Tenor (time bucket)
4-Volatility time bucket (if applicable)
5-Strike bucket (if applicable)
1
Regulators input scenario
1-Interest rates
2-Exchange rate
3-Volatility
4-Correlation
Term Structure of
Asset/Liabilities
by currency
2
Thomson Reuters
o Aggregated views foreign exchange claims
by time bucket
o Gap analysis (Asset/Liability mismatch)
o Sensitivity analysis (under scenarios)
o Volatility concentration matrices
o Strike/Barriers concentration matrices
3
o Central bank gets view of potential
bubbles
o Regulators can anticipate on
funding issues per currency and
instrument
o Aggregated risk view in GBP
o Banks can benchmark their funding
risk against industry view.
• Allow an assessment of firms’ currency liquidity risks and their potential
vulnerabilities to a drying up of certain currency swap markets
VARIABLE CAPITAL ADEQUACY RATIOS &
CROSS-SYSTEM SIMULATIONS
SIMULATE
SHOCKS
Market
Credit
Liquidity
Defaults
MEASURE
IMBALANCES
Asset/Liab benchmarks
Risk
Risk
concentrations
concentrations
ADJUST
Identify
Asset
Bubble
Assess
Risks
Liquidity
Liquidity risk
risk
Basis
Basis risk
risk
• Variable CAR
• Pro/Counter Cyclical
• Update instrument risk factors
• Communicate policies
• Recommandations
COMMUNICATE
Liquidity mismatches
Industry representatives
Regulators
Policy makers
Previsionists
CONSULT
• Combine modeling, human judgment and consensus based consultation
• Adjust regulatory policies according to risk intelligence
• Anticipate bubbles and eradicate systemic risk
Risk Intelligence is the New Efficient Frontier
Balancing shareholder value versus risk exposure depends on the firm’s
assessment of its aggregate sensitivity to risk factors under changing
conditions and on its ability to act upon it.
Regulators’ insights depend on risk intelligence.
Data
Information
Post Trade
Analysis Analytics
Collection
Data
Management
• Equity Prices
• Public Company
Fundamentals
• Beta
• Duration
• Single desk /
portfolio
• Unsophisticated
• Pricing &
Reference data
• Valuation Risk
• VaR
• Binomial Model
• Monte
Carlo VaR
• Evaluated
Pricing
• Risk Benchmarks
• Risk Indices
• Risk Ratings
• Potential
Future
Exposure
• Stress and
Scenario
Testing
• Multi desk / portfolio
• Static post-trade risk
aggregation
• Portfolio view of firm-wide risk
• Dynamic aggregation of
contextualized risk
Risk measurements
Insights
Stress Tests
&
Reverse
Risk
Aggregation
Risk Intelligence
Value
21
Risk Intelligence: The 21st Century Frontier of Market Efficiency
CRITICAL SUCCESS FACTORS IN IMA IMPLEMENTATION
PHILIPPE CARREL
Mumbai, July 21st 2010