Managerial Aspects of
Enterprise Risk Management
David L. Olson
University of Nebraska-Lincoln
Desheng Wu
University of Toronto; University of Reykjavik
Risk & Business
• Taking risk is fundamental to doing business
– Insurance
• Lloyd’s of London
– Hedging
• Risk exchange swaps
• Derivatives/options
• Catastrophe equity puts (cat-e-puts)
– ERM seeks to rationally manage these risks
• Be a Risk Shaper
Risk Reduction Strategies
C.S. Tang
Journal of Logistics: Research and Applications 9:1 [2006] 33-45
1.
2.
3.
4.
Identify different types of risk
Estimate likelihood of each event
Assess potential loss from major disruption
Identify strategies to reduce risk
Finland 2010
Another view
Slywotzky & Drzik, HBR [2005]
• Financial
– Currency fluctuation
• DEFENSE: Hedging
• Hazard
– Chemical spill
• DEFENSE: Insurance
• Operational
– Computer system failure
• DEFENSE: Backup (dispersion, firewalls)
• New technology overtaking your product
– ACE inhibitors, calcium channel blockers ate into hypertension drug
market of beta-blockers & diuretics
• Demand shifts
– Gradual – Oldsmobile; Rapid - Station wagons to Minivans
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Technology Shift
• Loss of patent protection
• Outdated manufacturing process
– DEFENSE: Double bet
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•
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Invest in multiple versions of technology
Microsoft: OS/2 & Windows
Intel: RISC & CISC
Motorola didn’t – Nokia, Samsung entered
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Brand Erosion
• Perrier – contamination
• Firestone – Ford Explorer
• GM Saturn – not enough new models
– DEFENSE: Redefine scope
• Emphasize service, quality
– DEFENSE: Reallocate brand investment
• AMEX – responded to VISA campaign, reduced
transaction fees, sped up payments, more ads
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One-of-a-kind Competitor
• Competitor redefines market
• Wal-Mart
– DEFENSE: Create new, non-overlapping business
design
• Target – unique product selection
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Customer Priority Shift
– DEFENSE: Analyze proprietary information
• Identify next customer shift
– Coach leather goods – competes with Gucci
– Went trendy, aggressive in-market testing
» Customer interviews, in-store product tests
– DEFENSE: Market experiments
• Capital One – 65,000 experiments annually
– Identify ever-smaller customer segments for credit cards
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New Project Failure
• Edsel
– DEFENSE: Initial analysis
• Best defense
– DEFENSE: Smart sequencing
• Do better-controllable projects first
– Applied Materials – chip-making
– DEFENSE: Develop excess options
• Improve odds of eventual success
– Toyota – hybrid: proliferation of Prius options
– DEFENSE: Stepping-stone method
• Create series of projects
– Toyota – rolling out Prius
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DEALING WITH RISK
• Management responsible for ALL risks facing
an organization
• CANNOT POSSIBLY EXPECT TO ANTICIPATE ALL
• AVOID SEEKING OPTIMAL PROFIT THROUGH
ARBITRAGE
• FOCUS ON CONTINGENCY PLANNING
– CONSIDER MULTIPLE CRITERIA
– MISTRUST MODELS
Financial Risk Management
• Evaluate chance of loss
– PLAN
• Hubbard [2009]: identification, assessment,
prioritization of risks followed by coordinated
and economical application of resources to
minimize, monitor, and control the probability
and/or impact of unfortunate events
– WATCH, DO SOMETHING
Value-at-Risk
• One of most widely used models in financial
risk management (Gordon [2009])
• Maximum expected loss over given time
horizon at given confidence level
– Typically how much would you expect to lose 99%
of the time over the next day (typical trading
horizon)
• Implication – will do worse (1-0.99) proportion of the
time
VaR = 0.64
expect to exceed 99% of time in 1 year
Here loss = 10 – 0.64 = 9.36
Finland 2010
Use
• Basel Capital Accord
– Banks encouraged to use internal models to measure
VaR
– Use to ensure capital adequacy (liquidity)
– Compute daily at 99th percentile
• Can use others
– Minimum price shock equivalent to 10 trading days
(holding period)
– Historical observation period ≥1 year
– Capital charge ≥ 3 x average daily VaR of last 60
business days
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Limits
• At 99% level, will exceed 3-4 times per year
• Distributions have fat tails
• Only considers probability of loss – not
magnitude
• Conditional Value-At-Risk
– Weighted average between VaR & losses
exceeding VaR
– Aim to reduce probability a portfolio will incur
large losses
Finland 2010
Correlation Makes a Difference
Daily Models t-distribution
0.80
0.70
0.60
0.50
Return(correlated)
0.40
Return(uncorrelated)
0.30
0.20
0.10
0.00
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Correlation impact on Variance
Daily Models t-distribution
3 outliers – China mixed with others
1600.00
1400.00
1200.00
1000.00
Return(correlated)
800.00
Variance(correlated)
Variance(uncorrelated)
600.00
400.00
200.00
0.00
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Correlation impact on Value-at-Risk
Daily Models t-distribution
Directly proportional to Variance
120.00
100.00
80.00
Return(correlated)
60.00
VaR(correlated)
VaR(uncorrelated)
40.00
20.00
0.00
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Conclusions
• Can use a variety of models to plan portfolio
• Expect results to be jittery
– Near-optimal may turn out better
– Sensitive to distribution assumed
• Trade-off – risk & return
COSO
Committee of Sponsoring Organizations
Treadway Committee – 1990s
Smiechewicz [2001]
• Assign responsibility
– Board of directors
• Establish organization’s risk appetite
• establish audit & risk management policies
– Executives assume ownership
• Policies express position on integrity, ethics
• Responsibilities for insurance, auditing, loan review, credit, legal
compliance, quality, security
• Common language
– Risk definitions specific to organization
• Value-adding framework
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COSO Integrated Framework 2004
Levinsohn [2004]; Bowling & Rieger [2005]
• Internal environment – describe domain
• Objective setting – objectives consistent with
mission, risk appetite
• Event identification – risks/opportunities
• Risk assessment - analysis
• Risk response – based on risk tolerance & appetite
• Control activities
• Information & communication – to responsible
people
• Monitoring
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Supply Chain Risk Categories
6 sources
CATEGORY
RISK
NATURE
External
Natural disaster, plant fire, disease & epidemics
POLITICAL SYSTEM
“
War, terrorism, labor disputes, regulations
COMPETITOR & MARKET “
Price, recession, exchange rate
Demand, customer payment
New technology, obsolescence substitutes
AVAILABLE CAPACITY
Internal
Capacity cost, supplier bankruptcy
INTERNAL OPERATION
“
Forecast inaccuracy, safety
Bullwhip, agility, on-time delivery
Tradeoff: inventory/fill rate
Quality
INFORMATION SYSTEM
“
System breakdown
Distorted information
Integration
Viruses/bugs/hackers
Finland 2010
Supply Chain risk management process
P. Chapman, M. Cristopher, U. Juttner, H. Peck, R. Wilding,
Logistics and Transportation Focus 4:4 [2002] 59-64
• Risk Identification
– Uncertainties: demand, supply, cost {quantitative}
– Disruption: disasters, economic crises {qualitative}
• Risk Assessment
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–
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Political
Product availability
Capacity, demand fluctuation
Technology, labor
Financial instability, management turnover
• Risk Avoidance
– Insurance
– Inventory buffers
– Supply chain alliances, e-procurement
• Risk Mitigation
– Product pricing, other demand control
– Product variety
– VMI, CPFR
Finland 2010
Empirical
• BUBBLES
– Dutch tulip mania – early 17th Century
– South Sea Company – 1711-1720
– Mississippi Company – 1719-1720
• Isaac Newton got burned: “I can calculate the motion
of heavenly bodies but not the madness of people.”
Modern Bubbles
• London Market Exchange (LMX) spiral
– 1983 excess-of-loss reinsurance popular
– Syndicates ended up paying themselves to insure
themselves against ruin
– Viewed risks as independent
• WEREN’T: hedging cycle among same pool of insurers
– Hurricane Alicia in 1983 stretched the system
Long Term Capital Management
• Black-Scholes – model pricing derivatives
• LTCM formed to take advantage
– Heavy cost to participate
– Did fabulously well
• 1998 invested in Russian banks
– Russian banks collapsed
– LTCM bailed out by US Fed
• LTCM too big to allow to collapse
Information Technology
• 1990s very hot profession
• Venture capital threw money at Internet ideas
– Stock prices skyrocketed
– IPOs made many very rich nerds
– Most failed
• 2002 bubble burst
– IT industry still in trouble
• ERP, outsourcing
Real Estate
• Considered safest investment around
– 1981 deregulation
• In some places (California) consistent high rates of
price inflation
– Banks eager to invest in mortgages – created tranches of
mortgage portfolios
• 2008 – interest rates fell
– Soon many risky mortgages cost more than houses worth
– SUBPRIME MORTGAGE COLLAPSE
– Risk avoidance system so interconnected that most banks
at risk
APPROACHES TO THE PROBLEM
• MAKE THE MODELS BETTER
– The economic theoretical way
– But human systems too complex to completely
capture
– Black-Scholes a good example
• PRACTICAL ALTERNATIVES
– Buffett
– Soros
Better Models
Cooper [2008]
• Efficient market hypothesis
– Inaccurate description of real markets
– disregards bubbles
• FAT TAILS
• Hyman Minsky [2008]
– Financial instability hypothesis
• Markets can generate waves of credit expansion, asset inflation,
reverse
• Positive feedback leads to wild swings
• Need central banking control
• Mandelbrot & Hudson [2004]
– Fractal models
• Better description of real market swings
Fat Tails
• Investors tend to assume normal distribution
– Real investment data bell shaped
– Normal distribution well-developed, widely understood
• TALEB [2007]
– BLACK SWANS
– Humans tend to assume if they haven’t seen it, it’s impossible
• BUT REAL INVESTMENT DATA OFF AT EXTREMES
– Rare events have higher probability of occurring than normal
distribution would imply
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•
•
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Power-Log distribution
Student-t
Logistic
Normal
Human Cognitive Psychology
• Kahneman & Tversky [many – c. 1980]
– Human decision making fraught with biases
• Often lead to irrational choices
• FRAMING – biased by recent observations
– Risk-averse if winning
– Risk-seeking if losing
• RARE EVENTS – we overestimate probability of rare
events
– We fear the next asteroid
– Airline security processing
Animal Spirits
• Akerlof & Shiller [2009]
– Standard economic theory makes too many
assumptions
• Decision makers consider all available options
• Evaluate outcomes of each option
– Advantages, probabilities
• Optimize expected results
– Akerlof & Shiller propose
• Consideration of objectives in addition to profit
• Altruism - fairness
Warren Buffett
• Conservative investment view
– There is an underlying worth (value) to each firm
– Stock market prices vary from that worth
– BUY UNDERPRICED FIRMS
– HOLD
• At least until your confidence is shaken
– ONLY INVEST IN THINGS YOU UNDERSTAND
• NOT INCOMPATIBLE WITH EMT
George Soros
• Humans fallable
• Bubbles examples reflexivity
– Human decisions affect data they analyze for future
decisions
– Human nature to join the band-wagon
– Causes bubble
– Some shock brings down prices
• JUMP ON INITIAL BUBBLE-FORMING
INVESTMENT OPPORTUNITIES
– Help the bubble along
– WHEN NEAR BURSTING, BAIL OUT
Nassim Taleb
• Black Swans
– Human fallability in cognitive understanding
– Investors considered successful in bubble-forming
period are headed for disaster
• BLOW-Ups
• There is no profit in joining the band-wagon
– Seek investments where everyone else is wrong
• Seek High-payoff on these long shots
– Lottery-investment approach
• Except the odds in your favor
Taleb Statistical View
• Mathematics
– Fair coin flips have a 50/50 probability of heads or
tails
– If you observe 99 heads in succession, probability of
heads on next toss = 0.5
• CASINO VIEW
– If you observe 99 heads in succession, probably the
flipper is crooked
• MAKE SURE STATISTICS ARE APPROPRIATE TO
DECISION
CASINO RISK
• Have game outcomes down to a science
• ACTUAL DISASTERS
1. A tiger bit Siegfried or Roy – loss about $100 million
2. A contractor suffered in constructing a hotel annex,
sued, lost – tried to dynamite casino
3. Casinos required to file with Internal Revenue
Service – an employee failed to do that for years –
Casino had to pay huge fine (risked license)
4. Casino owner’s daughter kidnapped – he violated
gambling laws to use casino money to raise ransom
Risk Management Tools
• Simulation (Beneda [2005])
– Monte Carlo – Crystal Ball
• Multiple criteria analysis
– Tradeoffs between risk & return
• Balanced Scorecard
– Organizational performance measurement
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