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  1. Broader Perspectives of RISK MANAGEMENTFinancial – Information Systems – Supply Chain David L. Olson University of Nebraska Desheng Wu University of Toronto; University of Reykjavik 3-C Risk Forum 2011

  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-C Risk Forum 2011

  3. 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

  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 3-C Risk Forum 2011

  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.” 3-C Risk Forum 2011

  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 • WEREN’T: hedging cycle among same pool of insurers • Hurricane Alicia in 1983 stretched the system 3-C Risk Forum 2011

  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 3-C Risk Forum 2011

  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 3-C Risk Forum 2011

  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 3-C Risk Forum 2011

  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 3-C Risk Forum 2011

  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 3-C Risk Forum 2011

  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 3-C Risk Forum 2011

  13. 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 3-C Risk Forum 2011

  14. 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

  15. 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 3-C Risk Forum 2011

  16. 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 3-C Risk Forum 2011

  17. 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

  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 3-C Risk Forum 2011

  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 3-C Risk Forum 2011

  20. VaR = 0.64expect to exceed 99% of time in 1 yearHere loss = 10 – 0.64 = 9.36 3-C Risk Forum 2011

  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 3-C Risk Forum 2011

  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 3-C Risk Forum 2011

  23. 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

  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 3-C Risk Forum 2011

  25. Impact of Distribution on VaRFat tails matter 3-C Risk Forum 2011

  26. Correlation Makes a DifferenceDaily Models t-distribution 3-C Risk Forum 2011

  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 3-C Risk Forum 2011

  28. 12 Investment Opportunitiesdaily data – 6/14/2000 to 7/6/2009Change 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 3-C Risk Forum 2011

  29. Pre- & Post-2008 3-C Risk Forum 2011

  30. 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 3-C Risk Forum 2011

  31. 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 3-C Risk Forum 2011

  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 (assets>price); • holds until convinced otherwise • Avoids buying what he doesn’t understand (IT) 3-C Risk Forum 2011

  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 3-C Risk Forum 2011

  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 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

  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 3-C Risk Forum 2011

  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 3-C Risk Forum 2011

  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 3-C Risk Forum 2011

  38. Risk Management & IT, Supply Chains 3-C Risk Forum 2011

  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 3-C Risk Forum 2011

  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 3-C Risk Forum 2011

  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 3-C Risk Forum 2011

  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 3-C Risk Forum 2011

  43. Supply Chain Risk Sources • Giunipero, AlyEltantawy [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

  44. 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 3-C Risk Forum 2011

  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 3-C Risk Forum 2011

  46. 2010 Springer 3-C Risk Forum 2011

  47. Monte Carlo Simulation 3-C Risk Forum 2011

  48. China vendor price distribution 3-C Risk Forum 2011

  49. Taiwan vendor price distribution 3-C Risk Forum 2011

  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 = {(1+Kkjsjk) - 1}/K 3-C Risk Forum 2011