Managerial Aspects of Enterprise Risk Management

David L. OlsonUniversity of Nebraska-Lincoln

Desheng WuUniversity 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 StrategiesC.S. Tang

Journal of Logistics: Research and Applications 9:1 [2006] 33-45

1. Identify different types of risk2. Estimate likelihood of each event3. Assess potential loss from major disruption4. Identify strategies to reduce risk

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Another viewSlywotzky & 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• Loss of patent protection• Outdated manufacturing process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• 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 = 0.64expect 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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Correlation Makes a DifferenceDaily Models t-distribution

Correlation impact on VarianceDaily Models t-distribution

3 outliers – China mixed with others

Correlation impact on Value-at-RiskDaily Models t-distribution

Directly proportional to Variance

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

COSOCommittee of Sponsoring Organizations

Treadway Committee – 1990sSmiechewicz [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 2004Levinsohn [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 Categories6 sources

CATEGORY RISK

NATURE External Natural disaster, plant fire, disease & epidemics

POLITICAL SYSTEM “ War, terrorism, labor disputes, regulations

COMPETITOR & MARKET “ Price, recession, exchange rateDemand, customer paymentNew technology, obsolescence substitutes

AVAILABLE CAPACITY Internal Capacity cost, supplier bankruptcy

INTERNAL OPERATION “ Forecast inaccuracy, safetyBullwhip, agility, on-time deliveryTradeoff: inventory/fill rateQuality

INFORMATION SYSTEM “ System breakdownDistorted informationIntegrationViruses/bugs/hackers

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Supply Chain risk management processP. 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– 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

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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.”

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 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

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

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 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

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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