1. Multiple Criteria Philosophy and Value-at-Risk David L. Olson – University of Nebraska Desheng Wu – University of Toronto; University of Reykjavik MCDM2011

2. Focus The philosophy part – PARETO OPTIMALITY The enterprise risk management part – VAR – Treatment of investment risk – Problems Models and assumptions If you have enough criteria, practically all choices will be Pareto Optimal MCDM2011

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) MCDM2011

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 MCDM2011

5. Empirical BUBBLES – Dutch tulip mania – early 17 th 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.” MCDM2011

6. 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 MCDM2011

7. 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 MCDM2011

8. “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 MCDM2011

9. Credit Default Swap Nocera & 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 MCDM2011

10. Circa 2005 Nocera & 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 MCDM2011

11. Ratings Nocera & 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 MCDM2011

12. Ratings Failures Nocera & 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 MCDM2011

13. Mortgage Abuses Nocera & 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 MCDM2011

14. 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 MCDM2011

15. PRACTICAL ALTERNATIVES Warren Buffet George Soros MCDM2011

16. 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 MCDM2011

17. 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 MCDM2011

18. 12 Investment Opportunities daily data – 6/14/2000 to 7/6/2009 Change each day from prior Mean, 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 MCDM2011

19. Idea Identify Pareto optimal set – 2 criteria Maximize mean (return) Minimize standard deviation (risk) – 3 criteria Avoid Chinese (China, HongKong) – 4 criteria Avoid US (USA1, USA2, Treasury, DowJ, Royce Focus) MCDM2011

20. Data – 2 Criteria Min VarCC@0.6CC@0.8CC@0.9CC@0.95Max Return World0.023 00.0050.0110.0140 USA10 00000 USA20 00000 China0.011 0.0220.0140.0130.0120 Europe0 00000 Japan0.016 0.0050.0130.014 0 HongKong0 0.0060000 Treasury0.031 0.0250.030 0 DowJ0.002 0.0100.0180.0120.0100 Royce0 0.0140.001001 Berkshire0.031 0.0420.0340.033 0 Fidelity0.887 0.8760.8850.886 0 MCDM2011

21. Data Additional Criteria 1 to 4 criteriaAdd 5 th (max China)Add 6 th (min US) World Nondominated USA1 Dominated USA2 Dominated China Nondominated Europe DominatedWeak nondom Japan Nondominated HongKong Nondominated Treasury Nondominated DowJ Nondominated Royce Nondominated Berkshire Nondominated Fidelity Nondominated MCDM2011

22. POINT Investments will be portfolios – Mixtures of investments The data still demonstrates the point – IF YOU INCLUDE ENOUGH CRITERIA, HARD TO FIND DOMINATED SOLUTIONS – There must be a reason the market cleared Keeney MAUT models – Typically 80 criteria Government choices – Whatever is first choice, hearings will stifle MCDM2011

23. Better Models Cooper [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 MCDM2011

24. 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) MCDM2011

25. 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 MCDM2011

26. 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 MCDM2011

27. 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 MCDM2011

28. 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 MCDM2011

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 MCDM2011