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Managerial Aspects of Enterprise Risk Management

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

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Managerial Aspects of Enterprise Risk Management

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  1. Managerial Aspects of Enterprise Risk Management David L. Olson University of Nebraska-Lincoln Desheng Wu University of Toronto; University of Reykjavik

  2. 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. Risk Reduction StrategiesC.S. TangJournal of Logistics: Research and Applications 9:1 [2006] 33-45 • Identify different types of risk • Estimate likelihood of each event • Assess potential loss from major disruption • Identify strategies to reduce risk Finland 2010

  4. 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 Finland May 2010

  5. Technology Shift • Loss of patent protection • Outdated manufacturing process • DEFENSE: Double bet • Invest in multiple versions of technology • Microsoft: OS/2 & Windows • Intel: RISC & CISC • Motorola didn’t –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 Finland May 2010

  6. 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 Finland May 2010

  7. One-of-a-kind Competitor • Competitor redefines market • Wal-Mart • DEFENSE: Create new, non-overlapping business design • Target – unique product selection Finland May 2010

  8. 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 Finland May 2010

  9. 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 Finland May 2010

  10. 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

  11. 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

  12. 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

  13. VaR = 0.64expect to exceed 99% of time in 1 yearHere loss = 10 – 0.64 = 9.36 Finland 2010

  14. 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 Finland 2010

  15. 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

  16. Correlation Makes a DifferenceDaily Models t-distribution

  17. Correlation impact on VarianceDaily Models t-distribution3 outliers – China mixed with others

  18. Correlation impact on Value-at-RiskDaily Models t-distributionDirectly proportional to Variance

  19. 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

  20. COSOCommittee of Sponsoring OrganizationsTreadway 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 Finland May 2010

  21. 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 Finland May 2010

  22. Supply Chain Risk Categories6 sources Finland 2010

  23. 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 Finland 2010

  24. 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.”

  25. 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

  26. 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

  27. 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

  28. 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

  29. 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

  30. 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

  31. 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

  32. 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

  33. 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

  34. 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

  35. 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

  36. 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

  37. 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

  38. 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 owner’s daughter kidnapped – he violated gambling laws to use casino money to raise ransom

  39. Risk Management Tools • Simulation (Beneda [2005]) • Monte Carlo – Crystal Ball • Multiple criteria analysis • Tradeoffs between risk & return • Balanced Scorecard • Organizational performance measurement Finland May 2010

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