Broader Perspectives of
RISK MANAGEMENT
Financial – Information Systems – Supply Chain
David L. Olson
University of Nebraska
Desheng Wu
University of Toronto; University of Reykjavik
3-C Risk Forum 2011
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
3-C Risk Forum 2011
Economic Philosophy of Risk
• Thűnen [1826]
– Profit is in part payment for assuming risk
• Hawley [1907]
– Risk-taking essential for an entrepreneur
• Knight [1921]
– Uncertainty non-quantitative
– Risk: measurable uncertainty (subjective)
– Profit is due to assuming risk (objective)
3-C Risk Forum 2011
Contemporary Economics
• Harry Markowitz [1952]
– RISK IS VARIANCE
– Efficient frontier – tradeoff of risk, return
– Correlations – diversify
• William Sharpe [1970]
– Capital asset pricing model
• Evaluate investments in terms of risk & return relative to the market
as a whole
• The riskier a stock, the greater profit potential
• Thus RISK IS OPPORTUNITY
• Eugene Fama [1965]
– Efficient market theory
• market price incorporates perfect information
• Random walks in price around equilibrium value
3-C Risk Forum 2011
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.”
3-C Risk Forum 2011
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
3-C Risk Forum 2011
Black Monday
• October 19, 1987
• Stock Exchange – triple witching hour
• Some blamed portfolio insurance
– Based on efficient-market theory, computer
trading models sought temporary diversions from
fundamental value
3-C Risk Forum 2011
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
3-C Risk Forum 2011
Correlated Investments
• EMT assumes independence across
investments
– DIVERSIFY – invest in countercyclical products
– LMX spiral blamed on assuming independence of
risk probabilities
– LTCM blamed on misunderstanding of investment
independence
3-C Risk Forum 2011
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
3-C Risk Forum 2011
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
3-C Risk Forum 2011
“All the Devils Are Here”
Nocera & McLean, 2010
• Circa 2005 – Financial industry urge to
optimize
– J.P. Morgan, other banks hired mathematicians,
physicists, rocket scientists, to create complex risk
models & products
• Credit default swap – derivatives based on
Value at Risk models
– One measure of market risk from one day to the
next – MAX EXPOSURE at given probability
3-C Risk Forum 2011
Credit Default Swap
Nocera & McLean, 2010
• 1994 J.P. Morgan
– Exxon Valdez oil spill
– Exxon faced possible $5 billion fine
• Drew on $4.8 billion line of credit from J.P. Morgan
• Morgan couldn’t alienate Exxon
– But loan would tied up lots of money
• Morgan got European Bank for Reconstruction &
Development to swap default risk for the loan for a fee
3-C Risk Forum 2011
Circa 2005
Nocera & McLean, 2010
• Banks want more profit
– Create products to sell to investors
• Mortgage granting agencies want fees
– Don’t worry about risk – sell to Wall Street
• Wall Street packages different mortgages into
CDOs (collateralized debt obligations)
• Prior to 2007 – CDOs consisted of corporate debt
• 2007 – shifted to mortgage debt
– Blending mortgages of different grades, locations, intended to
diversity
– View that high return required high risk
– Needed AAA rating to attract investors
Ratings
Nocera & McLean, 2010
• Prior to 1970s, ratings agencies gained
revenue from subscribers
– Subscription optional
• 1970s – switched to charging issuers directly
– Investors wouldn’t buy unrated bonds
– Issuers required to get ratings
– CONFLICT OF INTEREST
• SEC decreed Moody’s, S&P, Fitch were
qualified to rate bonds
3-C Risk Forum 2011
Ratings Failures
Nocera & McLean, 2010
• 1929 -78% of AA or AAA municipal bonds
defaulted
• 1970s Penn Central RR
• Near default of New York City
• Bankruptcy of Orange County
• Asian, Russian meltdowns
• 1990s – Long-Term Capital Management
3-C Risk Forum 2011
Mortgage Abuses
Nocera & McLean, 2010
• Loan officers often convinced applicants to lie
• Part-time housekeeper earning ≈$1,300/mo
– fronted for sister, got loan
– unable to find steady work so returned to Poland
• Dairy milker earning ≈$1,000/mo purported to be
foreman earning $10,500/mo
– Didn’t speak English
– Bought house for son
– Told by lender that he was lending his credit to his son
• Janitor earning $3,900/mo
– Claimed to be account executive (for nonexistent firm)
– Closed loan on $600,000 house
– Never made $30,000 down payment Originator claimed
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
3-C Risk Forum 2011
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
3-C Risk Forum 2011
VaR = 0.64
expect to exceed 99% of time in 1 year
Here loss = 10 – 0.64 = 9.36
3-C Risk Forum 2011
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
3-C Risk Forum 2011
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
3-C Risk Forum 2011
Demonstration Data
• 5 stock indexes
– Morgan Stanley World Index (MSCI)
– New York Stock Exchange Composite Index (NYSE)
– Standard & Poors 500 (S&P)
– Shenzhen Composite (China)
– Eurostoxx 50 (Euro)
3-C Risk Forum 2011
Distributions
• Used Crystal Ball software
– Chi-squared, Kolmogorov-Smirnov, AndersonDarling for goodness of fit
• Results stable across methods
• Student-t best fit
– Logistic 2nd, Normal & Lognormal 3rd or 4th
– IMPLICATION:
• Fat tails exist
• Symmetric
3-C Risk Forum 2011
Impact of Distribution on VaR
Fat tails matter
120
100
80
Return
VaR(t)
60
VaR(logistic)
VaR(normal)
40
20
0
1
2
3
4
5
6
7
8
9
10
11
12
13
3-C Risk Forum 2011
14
15
16
17
18
19
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
1
2
3
4
5
6
7
8
9
10
11
12
13
3-C Risk Forum 2011
14
15
16
17
18
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
3-C Risk Forum 2011
12 Investment Opportunities
daily data – 6/14/2000 to 7/6/2009
Change each day from prior
Mean, Standard Deviation, Avoid Chinese, Avoid US (except Berkshire)
•
•
•
•
•
•
•
World Index
• Hong Kong index
USA1
• Treasury Yield Bond
USA2
• DJSI World Index
Chinese index
• Royce Focus Fund
Eurostoxx
• Berkshire Hathaway
Japanese index
• Equal
20 Nondominated portfolios
3-C Risk Forum 2011
Pre- & Post-2008
Investment
Pre Mean
1.0000
Pre
StDev
0.0091
Pre
Min
0.9590
Pre
Max
1.0471
Post
Mean
0.9987
Post
StDev
0.0238
Post
Min
0.9294
Post
Max
1.0952
World Index
USA1
1.0002
0.0102
0.9541
1.0532
0.9988
0.0305
0.9027
1.1222
USA2
1.0000
0.0111
0.9508
1.0573
0.9999
0.0289
0.9097
1.1158
Chinese index
1.0003
0.0171
0.8808
1.0968
1.0013
0.0252
0.9315
1.0889
Eurostoxx
0.9999
0.0147
0.9262
1.0808
0.9989
0.0271
0.9212
1.1100
Japanese index
1.0000
0.0138
0.9071
1.0590
0.9991
0.0284
0.8859
1.0996
HongKong index
1.0003
0.0138
0.8633
1.1072
0.9997
0.0321
0.8730
1.1435
Treasury Yield Bond
0.9999
0.0100
0.9347
1.0420
1.0001
0.0240
0.9268
1.0930
DJSI World Index
1.0001
0.0099
0.9551
1.0519
0.9988
0.0255
0.9253
1.0924
Royce Focus Fund
1.0009
0.0181
0.9160
1.0943
0.9988
0.0404
0.8367
1.2000
Berkshire Hathaway
1.0005
0.0120
0.9260
1.0781
0.9988
0.0320
0.8791
1.1613
Averages
1.0002
0.0127
0.9248
1.0698
0.9994
0.0289
0.9019
1.1202
0.0000
0.027
0.0001
P-values (from t-tests)
0.003
3-C Risk Forum 2011
Modeling Investments Problematic
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
3-C Risk Forum 2011
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
3-C Risk Forum 2011
Models are Flawed
• Soros got rich taking advantage of flaws in
other peoples’ models
• Buffett is a contrarian investor
– In that he buys what he views as underpriced in
underlying long-run value (assets>price);
• holds until convinced otherwise
– Avoids buying what he doesn’t understand (IT)
3-C Risk Forum 2011
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
3-C Risk Forum 2011
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
•
•
•
•
Power-Log distribution
Student-t
Logistic
Normal
3-C Risk Forum 2011
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
3-C Risk Forum 2011
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
3-C Risk Forum 2011
Information Systems Risk
• Physical
– Flood, fire, etc.
• Intrusion
– Hackers, malicious invasion, disgruntled employees
• Function
– Inaccurate data
– Not providing needed data
• ERM contributions
– More anticipatory; Focus on potential risks, solutions
– COSO process framework
3-C Risk Forum 2011
Risk Management & IT, Supply
Chains
3-C Risk Forum 2011
IT & ERM
• Enterprise Risk Management
– IT perspectives
• Enterprise Risk Management, Olson & Wu, World
Scientific (2008)
• New Frontiers in Enterprise Risk Management, Olson &
Wu, eds. (contributions from 27 others)
– Includes three addressing IT
» Sarbanes-Oxley impact – Chang, Choy, Cooper, Lin
» IT outsourcing evaluation – Cao & Leggio
» IT outsourcing risk in China – Wu, Olson, Wu
– Enterprise Systems a major IT focus
3-C Risk Forum 2011
Supply Chain Perspective of ERM
• Historical vertical integration
– Standard Oil, US Steel, Alcoa
– Traditional military
• Control all aspects of the supply chain
• Contemporary
– Cooperative effort
• Common standards
• High competition
• Specialization
– Internet
• Service oriented architecture
3-C Risk Forum 2011
Supply Chain Problems
• Land Rover
– Key supplier insolvent, laid off 1000
• Dole 1998
– Hurricane Mitch hit banana plantations
• Ford
– 9/11/2001 suspended air delivery, closed 5 plants
• 1997 Indonesian Rupiah devalued 50%
– Blocked out of US supply chains
– Jakarta public transport reduced operations, high repair
parts
– Li & Fung shifted production from Indonesia to other Asian
sources
3-C Risk Forum 2011
More Problems
• Taiwan earthquake 1999
– Dell & Apple supply chains short components a few
weeks
• Apple had shortages
• Dell avoided problems through price incentives on
alternatives
• Philips semiconductor plant in New Mexico burnt
2000
– Ericsson lost sales revenue
– Nokia had designed modular components, obtained
alternative chips
3-C Risk Forum 2011
Supply Chain Risk Sources
• Giunipero, Aly Eltantawy [2004]
–
–
–
–
–
–
–
–
–
Political events
Product availability
Distance from source
Industry capacity
Demand fluctuation
Technology change
Labor market change
Financial instability
Management turnover
3-C Risk Forum 2011
Robust Strategies
Tang [2006]
•
•
•
•
Postponement – standardization, commonality, modular design
Strategic stock – safety stock for strategic items only
Flexible supply base – avoid sole sourcing
Economic supply incentives – subsidize key items, such as flu
vaccine
•
•
•
•
Flexible transportation – multi-carrier systems, alliances
Dynamic pricing & promotion – yield management
Dynamic assortment planning – influence demand
Silent product rollover – slow product introduction - Zara
3-C Risk Forum 2011
Risk Management Tools
• Simulation (Beneda [2005])
– Monte Carlo – Crystal Ball
• Multiple criteria optimization (Dash & Kajiji
[2005])
– Goal programming - tradeoffs
• SYSTEMS FAILURE METHOD
– Information Systems Project Management
• INFORMATION TECHNOLOGY
3-C Risk Forum 2011
2010 Springer
3-C Risk Forum 2011
Monte Carlo Simulation
Quoted
price
Exchange
distribution
Product
failure
China
0.82
No(1.3,.2)
0.10
0.15
0.05
2.13
Taiwan
1.36
No(1.03,.02)
0.01
0.01
0.10
1.81
Vietnam
0.85
No(1.1,.1)
0.15
0.25
0.05
2.51
Germany
3.20
No(1.05,.02)
0.01
0.02
0.01
3.43
Alabama
2.05
1
0.03
0.20
0.03
2.78
3-C Risk Forum 2011
Organizatio
nal failure
Political
failure
Expected
price
China vendor price distribution
3-C Risk Forum 2011
Taiwan vendor price distribution
3-C Risk Forum 2011
Multiple Criteria Analysis
measure value vj of alternative j
• identify what is important (hierarchy)
• identify RELATIVE importance (weights wk)
• identify how well each alternative does on each
criterion (score sjk)
• can be linear
vj =  wk sjk
• or nonlinear
vj = {(1+Kkjsjk) - 1}/K
3-C Risk Forum 2011
MCDM Weights
Criteria
Base 100
Base 10
Best (100)
Worst (10)
Average
Quality
100
60
0.2299
0.2308
0.23
Experience
90
55
0.2069
0.2115
0.21
Cost
85
50
0.1954
0.1923
0.19
Flexibility
60
40
0.1379
0.1538
0.14
Technical
50
30
0.1149
0.1154
0.11
Exchange
30
15
0.0690
0.0577
0.06
Capital
20
10
0.0460
0.0385
0.06
435
260
3-C Risk Forum 2011
Scores
Quality
Experience
Cost
Flexibility
Technical Exchange
Capital
China
Problems
2 years
0.82
High
Average
High
Weak
Taiwan
High
17 years
1.36
High
High
Moderate
High
Vietnam
Concerns
1 year
0.85
Low
Low
Moderate
Weak
Germany
High
5 years
3.20
Low
High
Moderate
High
Alabama
good
7 years
2.05
Low
High
None
Average
China
0.20
0.30
1.00
1.00
0.60
0.00
0.20
Taiwan
1.00
1.00
0.50
1.00
1.00
0.50
1.00
Vietnam
0.40
0.10
0.95
0.20
0.20
0.50
0.20
Germany
1.00
0.70
0.00
0.20
1.00
0.50
1.00
Alabama
0.70
0.90
0.30
0.20
1.00
1.00
0.50
3-C Risk Forum 2011
Values
Criteria
Weights
CHINA
TAIWAN
Quality
0.23
0.20
1.00
0.40
1.00
0.70
Experience
0.21
0.30
1.00
0.10
0.70
0.90
Cost
0.19
1.00
0.50
0.95
0.00
0.30
Flexibility
0.14
1.00
1.00
0.20
0.20
0.20
Technical
0.11
0.60
1.00
0.20
1.00
1.00
Exchange
0.06
0.00
0.50
0.50
0.50
1.00
Capital
0.06
0.20
1.00
0.20
1.00
0.50
Score
0.52
0.88
0.39
0.61
0.64
Rank
4
1
5
3
2
3-C Risk Forum 2011
VIETNAM
GERMANY
ALABAMA
Balanced Scorecard
Perspectives
Goals
Measures
Financial
Survive
Succeed
Prosper
Cash flow
Sales, growth, income
Increase in Market share, ROI
Customer
New products
Responsive supply
Preferred suppliers
Customer partnerships
% sales new products
On-time delivery
Share of key accounts’ purchases
# Cooperative engineering efforts
Internal
business
Technology capability
Manufacturing experience
Design productivity
New product innovation
Benchmark vs. competition
Cycle time, unit cost, yield
Engineering efficiency
Planned vs. actual schedule
Innovation &
learning
Technology leadership
Manufacturing learning
Product focus
Time to market
Time to develop next generation
Process time to maturity
% products yielding 80% sales
New product innovation vs. competition
3-C Risk Forum 2011
Practical View: 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
3-C Risk Forum 2011
Practical View: 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
3-C Risk Forum 2011
Views of Bubbles
Cohen [1997] Chaos view
Soros [2008]
Trigger
Inception
INVEST
Expansion
Acceleration
INVEST MORE
Rising prices
Reinforcement (pass challenges)
Overtrading
Mass trading
Twilight period
Doubt
Reversal point
Selling flood
Accelerated decline
Collapse
Crisis
3-C Risk Forum 2011
GET OUT
OPTIMAL GET OUT
TOO LATE
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
3-C Risk Forum 2011
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
3-C Risk Forum 2011
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
3-C Risk Forum 2011