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Broader Perspectives of RISK MANAGEMENT Financial – Information Systems – Supply Chain

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Title: Broader Perspectives of RISK MANAGEMENT Financial – Information Systems – Supply Chain


1
Broader Perspectives of RISK MANAGEMENT Financial
Information Systems Supply Chain
  • David L. Olson
  • University of Nebraska
  • Desheng Wu
  • University of Toronto University of Reykjavik

2
Risk Business
  • Taking risk is fundamental to doing business
  • Insurance
  • Lloyds 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
Economic Philosophy of Risk
  • Thunen 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)

4
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

5
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.

6
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
  • WERENT hedging cycle among same pool of
    insurers
  • Hurricane Alicia in 1983 stretched the system

7
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

8
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

9
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

10
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

11
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

12
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

13
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 couldnt 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

14
Circa 2005 Nocera McLean, 2010
  • Banks want more profit
  • Create products to sell to investors
  • Mortgage granting agencies want fees
  • Dont 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

15
Ratings Nocera McLean, 2010
  • Prior to 1970s, ratings agencies gained revenue
    from subscribers
  • Subscription optional
  • 1970s switched to charging issuers directly
  • Investors wouldnt buy unrated bonds
  • Issuers required to get ratings
  • CONFLICT OF INTEREST
  • SEC decreed Moodys, SP, Fitch were qualified to
    rate bonds

16
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

17
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
  • Didnt 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

18
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

19
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

20
VaR 0.64 expect to exceed 99 of time in 1
year Here loss 10 0.64 9.36
21
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

22
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

23
Demonstration Data
  • 5 stock indexes
  • Morgan Stanley World Index (MSCI)
  • New York Stock Exchange Composite Index (NYSE)
  • Standard Poors 500 (SP)
  • Shenzhen Composite (China)
  • Eurostoxx 50 (Euro)

24
Distributions
  • Used Crystal Ball software
  • Chi-squared, Kolmogorov-Smirnov, Anderson-Darling
    for goodness of fit
  • Results stable across methods
  • Student-t best fit
  • Logistic 2nd, Normal Lognormal 3rd or 4th
  • IMPLICATION
  • Fat tails exist
  • Symmetric

25
Impact of Distribution on VaR Fat tails matter
26
Correlation Makes a Difference Daily Models
t-distribution
27
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

28
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
  • USA1
  • USA2
  • Chinese index
  • Eurostoxx
  • Japanese index
  • 20 Nondominated portfolios
  • Hong Kong index
  • Treasury Yield Bond
  • DJSI World Index
  • Royce Focus Fund
  • Berkshire Hathaway
  • Equal

29
Pre- Post-2008
30
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

31
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

32
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 (assetsgtprice)
  • holds until convinced otherwise
  • Avoids buying what he doesnt understand (IT)

33
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

34
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 havent seen it,
    its 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

35
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

36
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

37
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

38
Risk Management IT, Supply Chains

39
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

40
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

41
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

42
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

43
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

44
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

45
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

46
2010 Springer
47
Monte Carlo Simulation
48
China vendor price distribution
49
Taiwan vendor price distribution
50
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 ?(1Kkjsjk) - 1/K

51
MCDM Weights
52
Scores
53
Values
54
Balanced Scorecard
55
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

56
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

57
Views of Bubbles
58
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

59
CASINO RISK
  • Have game outcomes down to a science
  • ACTUAL DISASTERS
  • A tiger bit Siegfried or Roy loss about 100
    million
  • A contractor suffered in constructing a hotel
    annex, sued, lost tried to dynamite casino
  • Casinos required to file with Internal Revenue
    Service an employee failed to do that for years
    Casino had to pay huge fine (risked license)
  • Casino owners daughter kidnapped he violated
    gambling laws to use casino money to raise ransom

60
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
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