May, 2010J. Rizzi, CapGen Financial(jrizzi@capgen.com)

Presentation to:

Risk Minds 2010

Behavioral Basis of the Market Crisis

(The ideas expressed herein are those of the author and not CapGen Financial)

Gregory Zuckerman

despite sophisticated models mapping past behavior

they lacked understanding of human behavior - they

need to go beyond statistics of past andinclude psychology of people effect asat their foundation markets are people and thepeople effect increases uncertaintyreminding us numbers alone are never enough

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The Setting

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US Dollar LIBOR-OIS SPREADS

(How many standard deviations is this?…)

(…Who cares?)

(Source: Malcom Knight, Rebuilding the Global Architecture of Financial Regulation, March, 2009)

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What Happened?(Do you want to believe what you see..)

(…Or what I am telling you?)

INVESTORS: extrapolated and believed housing prices would not fall

Government Monetary Policy Monitoring CRA Regulatory captureIncentives: unintended consequences Risk intermediation reduces risk oversight (originate to distribute model) Too big too fail (TBTF) socialize losses Principal/Agent: management captured by employeesUnderwriters: Too big to manage resulted in governance breakdownRating Agencies: Regulatory enforced oligopoly Diversification substitution Knowledge Systemic for Idiosyncratic Risk Pseudo objectivity: math without history equals disaster

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Structured Finance

(Structured finance as a compensation scheme….)

(…disguised as a business)

Issues: Structure vs. Underlying

Substitute systematic for diversifiable risk: default risk on adverse states

Ignored joint payoff distributions

Incorrect Gaussian Copula methodology: correlation

Lacked sufficient historical data on new underlying asset class: they

extrapolated on a limited sample based on good times

Mispriced: put on real estate index earn 3X more

Moral Hazard: perfect moral hazard product for issuers, underwriters, rating agencies and investors

Covering up strategic decline with Tail Risk

Up to 60% of large bank revenues in 2006 came from structured finance

The Problem

• Guided by selective memories and information

• Fail to consider what we believe to be false

• Influenced by the actions of others

• Confuse preferences with prediction

• Engage in self serving attribution

• Disregard non-conforming views

(Economic Capital is a lighthouse….)

(… for the soon to be shipwrecked)

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Some Behavioral Effects in Risk Management

• Hindsight and Confirmation: I knew it all along and ignore nonconforming evidence

• Anchoring: Unduly influenced by first impressions

• Sunk Costs: Doubling down

• Overconfidence: Infallibility of judgment. Gives raise to illusions of control

• Optimism: It will work out

• Availability: More weight given to events easily recalled

• Threshold: Once frequency drops below threshold it is ignored

• Pattern Seeking: Fooled by randomness. Gamblers fallacy

(Risk Management is the fig leaf …)

(… behind which risk taking takes place)

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Risk and Uncertainty(Those whom the gods wish to destroy….)

(…they first teach math)

Issue: Can you reduce future to quantifiable risks calculated from existing data?

Battling Beliefs – history as data and future as output Ergodic – future is statistical shadow of past Nonergodic – path dependent – history matters

Uncertainty vs. Risk – the future is uncertain not just risky Risk – calculate odds of game Uncertainty – game changes

Result – subjective beliefs of uncertain future Cannot calculate probability of rare events based on past Exposure vs. experiences

Consequence: illusion of control based on flawed risk models

A MAP on the Limits of Statistics

Considerations: Distributions and payoffs

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Normal (risk)

Fat tails/unknown(uncertainty)

Distribution

(…not the process)

Quadrant 4: Normal techniques fail. Alternatives to consider: Redundancy not optimization Avoid predication: focus on discipline and resiliency Time horizon is longer Moral Hazard: bonuses tied to hidden risks Metrics: standard metrics no longer work Volatility absence is not equal to risk absence Risk numbers are dangerous: framing

Simple Complex Payoffs

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(Source: N. Taleb)

Physical Sciences

Social Sciences

(We observe the data….)

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Humans and Markets

(In physics you play against God….)

Markets and Hurricanes: they are different (J. Meriwether)

Hurricanes are not more likely because more hurricane insurance is written. This is not true for financial markets.

An increase in financial insurance increases likelihood of disaster.Those who know you sold the insurance (will trade against you) canmake it happen.

In a crisis all that matters is who holds what and at what price.

Markets are more complex than casinos. The numbers on the Roulette wheel never change. Markets make no guarantee that yesterday’s odds will be the same tomorrow.

(… in markets you play against God’s creatures)

Decisions at Risk

Uncertainty Bias

Beyond the data experiencesExperiencesExposures

Black SwansRare Events Large ImpactExplainable

Over confidence

Illusion of control

Hindsight bias

Anchoring

Amplifiers

Incentives

Bureaucracy

Opaqueness

(It is not what we don’t know that gets us in trouble…)

(…it is what we know that ain’t so)

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Risk Management

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The Setting

DimensionsFrequency

ExposureExperience

Severity

Focus: High impact low probability events (HILPEs)HILPEs difficult to understand and frequently ignoredHistory proves HILPEs do happen and can threaten survival of the unprepared

IssuesStatistical: insufficient data

Behavioral: infrequency clouds perceptionRisk estimates anchored Disaster myopia

Social: reduced from regulations collapse once behavior changesGoodhart’s LawRisk Adaptation

(Performance – is it luck…)

(… or skill)

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Risk Management

ToolboxAvoidance IgnoreMitigateTransferEquity

Self insure

(Not just that risk management fails…)

(… but it can produce unintended consequences that amplify damages)

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Complex Financial Institutions

High Risk Systems: prone to endogenous normal (system) accidents. Manmade catastrophesComplex nonlinear interaction: inevitable but unpredictability uncertain

Branching pathsFeedback loopsJumps

Tight coupling: network effectsGovernance: prevent management

from imposing risks on organization for their own benefit

Policy Implications(A ) Tolerate and improve (B ) Restructure(C ) Abandon

Disaster recovery

Strategic and

management

Processing errors

Model risk

Simple

Complex

Tig

ht

Loose

A

B

CAlternative costs

Catastrophe loss potential

(It is the system…)

(… not the event)

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Problem and Solution

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(James Grant: In financial markets…)

(…all progress is cyclical not cumulative)

Problem: HIPLERare DecisionsDelayed FeedbackLimited Understanding

Solution: Firm Level: Prisoners DilemmaRegulatory Level: Capture

Conclusion

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Thinking About Risks: the Shift

(Organizati0ns are a social…)

ClassicalIndependentStationaryRationalGaussianFrictionlessConsistent beliefsLinear Risk RewardComplete InformationIndividualsRisk Objective FunctionEquilibriumShocksEfficient

NewMemoriesUnstableBiasFat tailsArbitrage limitsInconsistencyNonlinearAsymmetric InformationInstitutionsUncertaintyPrincipal-Agent ConflictsCreative DestructionEndogenousAdaptive

(…not a physical phenomena)

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Conclusion

• Risk is managed by people not mathematical models

• Accept randomness

• Discipline not predictions

• Expect the unexpected

• Avoid catastrophe risk

• Focus on what you know and insure against extremes

(Ignore behavioral finance…)

(… at your peril)

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