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

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 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.”

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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

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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

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“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 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

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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

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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

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

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

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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

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PRACTICAL ALTERNATIVES

• Warren Buffet• George Soros

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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 Opportunitiesdaily data – 6/14/2000 to 7/6/2009

Change 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

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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 CriteriaMin Var [email protected] [email protected] [email protected] [email protected] Max

Return

World 0.023 0 0.005 0.011 0.014 0

USA1 0 0 0 0 0 0

USA2 0 0 0 0 0 0

China 0.011 0.022 0.014 0.013 0.012 0

Europe 0 0 0 0 0 0

Japan 0.016 0.005 0.013 0.014 0.014 0

HongKong 0 0.006 0 0 0 0

Treasury 0.031 0.025 0.030 0.030 0.030 0

DowJ 0.002 0.010 0.018 0.012 0.010 0

Royce 0 0.014 0.001 0 0 1

Berkshire 0.031 0.042 0.034 0.033 0.033 0

Fidelity 0.887 0.876 0.885 0.886 0.886 0MCDM2011

Data Additional Criteria1 to 4 criteria Add 5th (max China) Add 6th (min US)

World Nondominated

USA1 Dominated

USA2 Dominated

China Nondominated

Europe Dominated Weak nondom Weak nondom

Japan Nondominated

HongKong Nondominated

Treasury Nondominated

DowJ Nondominated

Royce Nondominated

Berkshire Nondominated

Fidelity NondominatedMCDM2011

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

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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

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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)

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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

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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

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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

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