Broader Perspectives of RISK MANAGEMENT

Financial – Information Systems – Supply Chain

David L. OlsonUniversity of Nebraska

Desheng WuUniversity 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

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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)

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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

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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.”

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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

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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

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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

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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

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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

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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

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“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

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Credit Default SwapNocera & 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

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Circa 2005Nocera & 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

RatingsNocera & 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

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Ratings FailuresNocera & 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

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Mortgage AbusesNocera & 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

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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

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VaR = 0.64expect to exceed 99% of time in 1 year

Here loss = 10 – 0.64 = 9.36

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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

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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)

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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

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Impact of Distribution on VaRFat tails matter

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Correlation Makes a DifferenceDaily Models t-distribution

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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

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12 Investment Opportunitiesdaily data – 6/14/2000 to 7/6/2009

Change each day from priorMean, 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

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Pre- & Post-2008Investment Pre Mean Pre

StDevPre Min

Pre Max

Post Mean

Post StDev

Post Min

Post Max

World Index 1.0000 0.0091 0.9590 1.0471 0.9987 0.0238 0.9294 1.0952

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

P-values (from t-tests) 0.003 0.0000 0.027 0.0001

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Modeling Investments ProblematicAPPROACHES 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

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Better ModelsCooper [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

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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)

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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

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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

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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

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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

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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

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Risk Management & IT, Supply Chains

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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 focus3-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

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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

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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

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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

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Robust StrategiesTang [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

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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

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2010 Springer

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Monte Carlo SimulationQuoted price

Exchange distribution

Product failure

Organizational failure

Political failure

Expected price

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

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China vendor price distribution

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Taiwan vendor price distribution

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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

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MCDM WeightsCriteria 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

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ScoresQuality 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

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ValuesCriteria Weights CHINA TAIWAN VIETNAM GERMANY ALABAMA

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

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Balanced ScorecardPerspectives Goals Measures

Financial SurviveSucceedProsper

Cash flowSales, growth, incomeIncrease in Market share, ROI

Customer New productsResponsive supplyPreferred suppliersCustomer partnerships

% sales new productsOn-time deliveryShare of key accounts’ purchases# Cooperative engineering efforts

Internal business

Technology capabilityManufacturing experienceDesign productivityNew product innovation

Benchmark vs. competitionCycle time, unit cost, yieldEngineering efficiencyPlanned vs. actual schedule

Innovation & learning

Technology leadershipManufacturing learningProduct focusTime to market

Time to develop next generationProcess time to maturity% products yielding 80% salesNew product innovation vs. competition

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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

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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

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Views of BubblesCohen [1997] Chaos view Soros [2008]

Trigger Inception INVEST

Expansion Acceleration INVEST MORE

Rising prices Reinforcement (pass challenges)

OvertradingMass trading

Twilight period GET OUT

Doubt Reversal point OPTIMAL GET OUT

Selling flood Accelerated decline TOO LATE

Collapse Crisis

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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

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CASINO RISK

• Have game outcomes down to a science• ACTUAL DISASTERS

1. A tiger bit Siegfried or Roy – loss about $100 million2. A contractor suffered in constructing a hotel annex,

sued, lost – tried to dynamite casino3. 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

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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

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