1 / 29

Multiple Criteria Philosophy and Value-at-Risk

Explore the philosophy and practical applications of Value-at-Risk (VAR) in decision-making and risk management. Learn from historical cases and key theories on risk, investment, and market behavior.

patriciarcurtis
Download Presentation

Multiple Criteria Philosophy and Value-at-Risk

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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 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.” 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 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 MCDM2011

  10. 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 MCDM2011

  11. 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 MCDM2011

  12. 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 MCDM2011

  13. 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 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 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 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 MCDM2011

  21. Data Additional Criteria 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 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 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

More Related