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Integrated Risk Management in a Financial Conglomerate. Til Schuermann* Federal Reserve Bank of New York World Bank Risk Management Workshop Cartage n a, Colombia February 17, 2004. - PowerPoint PPT Presentation
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Integrated Risk Management in aFinancial Conglomerate
Til Schuermann*Federal Reserve Bank of New York
World Bank Risk Management WorkshopCartagena, ColombiaFebruary 17, 2004
* Any views expressed represent those of the author only and not necessarily those of the Federal Reserve Bank of New York or the Federal Reserve System.
Filename 2
What Is a Financial Conglomerate?
Joint Forum definition (2001)
“Any group of companies under common control whose exclusive or predominant activities consists of providing significant services in at least two different financial sectors (banking, securities, insurance)”
Virtually all of the large, internationally active multinational financial institutions are, to some degree, financial conglomerates
– Strict “3 of 3” or weaker “2 of 3” definitions
Filename 3
Market Context
Rapid growth in scope of large, multi-line financial institutions
– Consolidation
– Financial deregulation
– Globalization
Not just bigger, also (much) more complex
Major advances in risk measurement and capital management practices across the industry
Capital regulation still largely based around single business lines or ‘silo’ approach
Filename 4
What Is the Regulatory Issue?
Banks, securities firms and insurance companies all conduct trading business with
– Many of the same instruments and
– Many of the same counterparties, but . . .
. . . subject to very different regulatory capital charges
Differences are profound and pervasive
– Differences in regulatory objectives
– Differences in definition of regulatory capital
– Differences in regulatory capital charges
Filename 5
Philosophical Differences About What Should Count as Capital
Differing assumptions about how to deal with a faltering firm– Securities regulators: liquidate without loss to customers or
recourse to bankruptcy proceedings, emphasis on subordinated claims
– Bank regulators: want time to detect and remediate, emphasis on patient money
– Insurance regulators: ring-fence for protection of customers, emphasis on adequacy of technical reserves
Evident in capital ratios– Securities firms: ~5%– Banks: ~ 10%– Insurers
• Life: ~8%• P&C: ~25%
Filename 6
Differing Definitions of Capital
Net Worth: similarities more apparent than real
– Mark to market accounting in securities firm
– Mix of mark to market & book value in banks
– Statutory accounting in insurance companies
Filename 7
Example of Different Treatments
Banking Regulation
• Treat as commercial loan
• BIS 1: 8% Capital
• BIS 2: 2% Capital
EU Credit Insurance
• Treat as credit insurance paying credit insurance premium of 1% pa.
• Solvency capital = 15% of premiums 0.16% of outstandings
EU Life Insurance
• Treat as investment
• Implicit asset charge = 3% of outstandings
Consider a credit exposure to an ‘A’ rated counterparty
Filename 8
Key Questions and Approach
1. How should assessments of capital adequacy take into account diversification or concentration of activities within a conglomerate?
2. What are the implications for regulating the solvency of a multi-line financial conglomerate?
Our Approach– Adopt a top-down economic perspective– Focus on unique problems of risk aggregation within a
conglomerate– Initially, make simplistic assumption that all risk types
have multivariate normal distribution– Risk is taken to be 99% VaR
Filename 9
Risk Types and Distributions
Credit Market P&C CAT P&C Experience Business Event
Operating RiskAsset Risk Liability Risk
RISK
Life
AA-
How to Aggregate?
Filename 10
Risk Types and Modeling Approaches
Risk Type Modeling Approach
Risk Type Modeling Approach
Market / ALM
VaR, scenario analysis
CAT Simulation, Exceedence Prob. Curves
Credit EL, UL; Simulation
Non-CAT P&C
Frequency Severity; Loss Triangles
Life Surplus Testing Operational Simulation, EVT
There is a large variety of measurement and modeling approaches
Filename 11
Risk Management in a Financial Conglomerate
Financial HoldingCompany
Financial HoldingCompany
CorrelationCorrelation
Very HighVery Low
ALM
Non-LicensedSubsidiary
Market
Credit
Insurance
Operating
P&C InsuranceCompany
Life InsuranceCompany
Insurance
UniversalBank
Market
Credit
ALM
Operating
ALM
Market
Credit
Insurance
Operating
Credit
Market
Insurance
ALM
Operating
Filename 12
Levels of Risk Aggregation in a Financial Institution
LEVEL 1Within a
Risk TypeCommercial
Credit
Consumer International
LEVEL 2Within a
Subsidiary
+
Market
+
Operating
LEVEL 3Across
Subsidiaries+
Bank Insurance
Filename 13
Diversification: Size of “Portfolio” and Degree of Correlation
Typical Market vs. Credit Risk Correlation
0%
20%
40%
60%
80%
100%
# of Positions
= 40%
= 2%
10050 150
Geographic Diversification
100%
78%
70%
45%
US US + UK US + UK +Germany
Global
Level 1
Filename 14
Diversification Across Risk Types
0%
20%
40%
60%
80%
100%
Standalone Diversified
Market/ALM: 20%
Credit 55%
Operat'l 25%
79%
Within Universal Bank
Level 2
Across Bank & Insurance
Bank + Insurance Diversified Across Business
100% 90%
Level 3
Filename 15
Diversification Across Risk Types: Financial Conglomerate
0%
2%
4%
6%
8%
10%
12%
Bank P&C-lite Bank Life-lite Bank-P&C Life Bank-lite Mixed
Div
ersi
fica
tio
n B
enef
it
Conservative Average
Filename 16
Summary of Results so Far
Diversification effects typically decrease at successive levels in an organization: Level 1 > Level 2 > Level 3
Provided standalone risks are correctly measured, incremental diversification benefits across banking and insurance fall into an expected range of 5-10%
Diversification effects are greatest when businesses are of similar size
Combining a bank with a P&C company produces the greatest diversification benefit because P&C and credit risks predominate and are uncorrelated
Filename 17
Alternative Interpretations
Naive View
• HCCAP Univ-BankCAP + InsCAP
• Level 3 diversification unimportant
• BIS 2 approach (deconsolidation) is right
Counter View
• Result only valid if regulatory measures at Levels 1 and 2 fully reflect diversification
• Existing measures are inherently limited:
– ‘Lowest common denominator’
– Assume ‘average’ correlation for many risk types at Level 1
– Ignore cross-factor diversification at Level 2
BUT
Risk Aggregation at Level 3 Can Only Be as Good as the Standalone Measures on Which It Is Based
Filename 18
Level 2: A More Sophisticated Approach Goal was to model market, credit and operational risk of a typical large,
internationally active bank
Market and credit risk distributions from market data
Operational risk distribution from industry (proprietary) database of operational risk events
Compare different ways of computing total risk distribution– Add-VaR: add-up marginal VaR to arrive at total
• Effectively BIS 2– Normal-VaR: assume joint normality– Copula-VaR: use copulas to arrive at total risk distribution– Hybrid approach: assume elliptical distribution (not as strict as joint
normal but almost as easy)
Risk is taken to be 99.9% VaR
Filename 19
Marginal Risk Distributions
Market risk distribution
0.000
0.001
0.002
0.003
0.004
0.005
0.006
0.007
-1.50% -1.06% -0.62% -0.19% 0.25% 0.69% 1.13% 1.56% 2.00%
Return (as % of trading book)
De
ns
ity
Credit risk distribution
0.000
0.002
0.004
0.006
0.008
0.010
0.012
-1.50% -1.06% -0.62% -0.19% 0.25% 0.69% 1.13% 1.56% 2.00%
Return (as % of lending book)
De
ns
ity
Operational risk distribution
0.000
0.020
0.040
0.060
0.080
0.100
0.120
0.140
0.160
0.180
-1.50% -1.06% -0.62% -0.19% 0.25% 0.69% 1.13% 1.56% 2.00%
Return (as % of total assets)
Den
sity
Market Credit Operational
Total risk distribution(default holding and correlation, normal copula)
0.000
0.002
0.004
0.006
0.008
0.010
0.012
0.014
0.016
loss -1.09% -0.88% -0.66% -0.44% -0.22% -0.01% 0.21% 0.43%
Return (as % of total book)
De
ns
ity
Total
Filename 20
Characteristics of Risk Distributions
Market
Credit
Operational
Highest volatility Lowest skewness Slightly fat tails
Moderate to high volatility Skewed Moderately fat-tailed
Low volatility Very skewed Very fat-tailed
Filename 21
Impact of Correlation at 99.9% VaR
-1.0%
-0.8%
-0.6%
-0.4%
-0.2%
0.0%
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8
Rho
To
tal
risk (
%o
f b
oo
k)
Add-VaR
Hybrid-VaR
Copula-VaR
Normal-VaR
(market,credit) = 50% vary to operational risk
Filename 22
Bottom Line Consistent ordering of approaches
– Add-VaR > Hybrid-VaR > Copula-VaR > Normal-VaR– Add-VaR biggest: imposes perfect inter-risk correlation– Normal-VaR smallest since it imposes thinnest tails– The Hybrid approach is strikingly close to copula-VaR
• Use volatility multiples from marginals• Incorporates correlation
Diversification benefits at 99.9% VaR can be substantial– Depending on correlations, 10% to 35%
As business mix or correlation shifts towards operational risk (very fat-tailed and skewed), 99.9% VaR increases dramatically
– Normal-VaR fails especially here– Hybrid approach can handle this well (sensitive to tail
shape of marginals)
Filename 23
What Has Been the Market Response?
Risk Measurement
• Economic capital increasingly being adopted as “common currency” for risk across financial businesses
• Migration of methodologies from banking to insurance
• Diversification effects captured at successive levels
• Customized models sensitive to business mix
Risk Management
• Conglomerates building up centralized risk and capital management units
• Dominant approach “hub and spoke” system
• Hub responsible for overseeing Group risk and capital planning (Level 3)
• Spokes responsible for risk management and transaction decisions within businesses (Levels 1 and 2)
Filename 26
Limitations of ‘Silo’ Regulation
Inconsistency
• Capital requirements dependent on where risk is booked
• Boundaries breaking down due to product innovation
• Increasing demand/potential for regulatory arbitrage
Aggregation
• Concentration of risks across legal boundaries
• Diversification across risk classes within a legal entity
• Diversification of risks across business activities and operating companies
Incompleteness
• Capital requirements of unlicensed operating companies
• Capital requirements/funding structure of holding company
• ‘Strategic’ investments in non-financial companies
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