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

This study examines the broader perspectives of risk management, focusing on financial information systems and supply chain risks. It analyzes the role of various factors such as insurance, hedging, derivatives, and options in managing these risks. The study also explores the economic philosophy of risk management and its contemporary implications. Furthermore, it discusses the impact of bubbles, market crashes, and correlated investments on risk management. The study also explores the role of information technology, real estate, and credit default swaps in risk management. Finally, it delves into the failures of ratings agencies and mortgage abuses, highlighting the need for improved risk avoidance systems.

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

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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

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