Transcript Views of Risk
Multiple Criteria Philosophy and Value-at-Risk
• • David L. Olson – University of Nebraska Desheng Wu – University of Toronto; University of Reykjavik MCDM2011
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
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
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
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
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
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
“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
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
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 –
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
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
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 –
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
PRACTICAL ALTERNATIVES
• • Warren Buffet George Soros MCDM2011
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
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
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
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
Data – 2 Criteria
World USA1 USA2 China Europe Japan HongKong Treasury DowJ Royce Berkshire Fidelity
0.023
0 0 0.011
0 0.016
0 0.031
0.002
0 0.031
0.887
Min Var [email protected]
0 0.022
0 0 0 0.005
0.006
0.025
0.010
0.014
0.042
0.876
0.005
0 0 0.014
0 0.013
0 0.030
0.018
0.001
0.034
0.885
MCDM2011
0.011
0 0 0.013
0 0.014
0 0.030
0.012
0 0.033
0.886
0.014
0 0 0.012
0 0.014
0 0.030
0.010
0 0.033
0.886
Max Return
1 0 0 0 0 0 0 0 0 0 0 0
World USA1 USA2 China Europe Japan HongKong Treasury DowJ Royce Berkshire Fidelity
Data Additional Criteria
1 to 4 criteria
Nondominated Dominated Dominated Nondominated Dominated Nondominated Nondominated Nondominated Nondominated Nondominated Nondominated Nondominated
Add 5
MCDM2011
th (max China) Add 6
Weak nondom
th (min US)
Weak nondom
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
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
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
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
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
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 –
events we overestimate probability of rare
– – We fear the next asteroid Airline security processing MCDM2011
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
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