Rethinking Risk Management Again
J. Rizzi
August, 2013
Draft #3 8-23-13
1
I.
Introduction
The 2008/2009 financial crisis represents a teachable moment for risk management.
In fact, it represents the second teachable moment. The first being Long Term Capital
Management, which occurred a decade earlier, and served as an unnoticed dress
rehearsal to the 21st century crisis. Large and small banks suffered severe damage
despite substantial pre-crisis risk management investments. The 2011 MF Global
Failure and the 2012 JPMorgan Chase London Whale $6B loss reminds us that
something is still wrong with risk management. Furthermore, regulatory changes like
Dodd Frank and BIS III are unlikely to improve matters. In fact, they may be
counterproductive as they change risk management into a compliance function.
Risk management’s prediction focus is largely based on recent history. It developed in
the 1990s based on an actuarial statistical approach to estimate future probabilities
based on past events. It involves looking backwards to see into the future. Tranquil
periods last long enough to seem to be the natural state. Crises seem sharp enough
to be seen as aberrations instead of normal accidents. An unfamiliar crisis is seen as
improbable, and not taken seriously. Substituting a probability distribution for
uncertainty does not solve the problem. Out of a sample of events; Peso Risk, caused
by unexpected regime changes, are difficult for individuals, regulators and
organizations to understand due to behavioral biases. These errors underlie the
failure of risk management and are reinforced by behavioral biases, such as
overconfidence.
The biases are magnified in financial institutions by the “Killer B’s” of budgets and
bonuses given that the budget bonus period is shorter than the risk horizon. The
combination of these biases produced an unwarranted belief that risk could be
controlled. This leads to the acceptance of what would otherwise be viewed as
unacceptable risk. Institutions mistakenly assumed that risk created return.
Therefore, risk appetite increases can create higher returns, while risk management
can handle the increased risk. Unfortunately, risk is not static. It evolves in ways not
fully understood.
Risk, the exposure to the consequences of uncertain events, is managed by people
and not by models. We must accept randomness by emphasizing discipline and
judgment over prediction. Markets are more complicated than many over-confident
risk managers believe.
This article reexamines risk management primarily from a bank viewpoint. It identifies
the errors in effective risk management that lead to massive losses at financial
institutions during the financial crisis. Also, it helps place risk management in its
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proper context within the organization versus being viewed as a detached specialist
function. The key is making risk count instead of just counting risk. Hopefully, it
illustrates how modern risk management techniques can create unintended
consequences in the management of financial institutions; how government policy and
corporate governance can influence the management of risk in financial institutions;
and how discipline and judgment are important in the management of risk.
II.
Delusion About Risk
Many bankers reach for yield in low return environments. Unfortunately, it has
potentially ruinous long-term side effects. The strategy is based on the mistaken belief
that risk creates return. Thus, if you cannot get desired returns from safe investments,
then increase your risk appetite and take more risk to increase your return.
Risk may be correlated with return. Risk is not, however, the raw material generating
return. If risk caused return, then it would not be risky, and Jimmy Cayne and Dick
Fuld, among others, would still be on Wall Street. The mistaken belief that risk creates
return confuses correlation with causation. Investors take risk and receive a return.
This does not mean they received a return because of risk. Rather, return is based on
skill in creating value and managing risk. Taking risk to generate nominal return,
however, is easier than creating a value added service that satisfies a client need.
Thus, absent strong board of directors’ oversight, managers will focus on nominal
returns as a key performance indicator instead of risk-adjusted return value drivers.
This encourages increased risk taking even though the institution is
undercompensated for the incremental risk.
Risk is a cost of return and not an opportunity. Seen in this light, it is something to be
reduced – not increased. Additionally, it has capital implications. Higher risk without
higher capital is like building in a flood plain without flood insurance. Capital is needed
to absorb the volatility. The increased capital reduces return on equity, which
illustrates risk alone does not increase value. In fact, many banks confuse investors
with high returns based on risk not skill. Ultimately, the reality becomes clear resulting
in “surprise” losses, shareholder value destruction and management changes.
We see only losses after risk is realized. Risk is usually and mistakenly measured by
extrapolating historical loss data to calculate a capital change known as economic
capital. The charge is deducted from an investment’s return to approximate a risk
adjusted return. The nonstationary business cycle related macro component of risk is
typically not reflected in the recent historical data and consequently is frequently
ignored. Consequently, economic capital is understated as many banks discovered
3
during the crisis. Losses tend to occur infrequently, but their effect is large. Since
there are usually more good years than bad, high-risk strategies can appear
successful for long periods of time. During a bull market, those who take on more risk
appear to outperform their more conservative peers.
Reliance on a trailing weighted average of returns misses periodic shocks which can
swamp shorter term averages. Not only does risk evolve over time, but so does
return. As popular asset classes, like commercial real estate pre-crisis and
commercial and industrial loans post crisis become crowded, returns fall requiring
even higher risk to maintain return levels.
Avoiding permanent capital loss through skillful risk reduction represents a competitive
advantage. Nonetheless, this fact received little attention pre-crisis during which time
many banks were seduced into thinking they could not lose. Also, higher short-term
nominal returns are more exciting than lower long-term risk adjusted returns,
especially for bonus purposes.
There is nothing wrong with high risk-high return strategies provided the volatility
implications are appreciated. The first cut is whether the institution has the skills
needed to manage risk to an acceptable scenario based stress level. Next, sufficient
capital and liquidity are required to withstand shocks.
The mechanical risk-return relationship inherent in the risk thermostat view of setting
risk appetite and business strategy is wrong. Sustainable long-term returns are
dependent on skill in managing risk, and not just in taking more risk. This is a subtle,
but critical distinction.
III.
Behavioral Finance Framework
Behavioral finance examines how managers gather, interpret, and process
information. It recognizes that models can influence behavior and shape decisions.
This influence can corrupt the decision process leading to suboptimal results.
Risk can be classified along two dimensions. The first concerns high-frequency
events with relatively clear cause-effect relationships. Other risks occur infrequently.
Consequently, the cause-effect relationship is unclear. The second dimension is
impact severity. No matter how remote, high-impact events cannot be ignored
because they can threaten an institution’s existence as was demonstrated in the
financial crisis. The dimensions are reflected in the risk map in Figure 1.
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Frequency
A
B
C
D
Impact
A:
B:
C:
D:
Figure 1
High frequency/low impact events: reflected in risk pricing.
Low frequency/low impact events: treated as a cost of business.
High frequency/high impact events: managed through control.
Low frequency/high impact events; frequently ignored.
Risk Map
Quadrant A events include retail credit products including credit cards. Many small
defaults are expected. Screening helps identify groups with higher default
probabilities. These groups are charged higher rates to offset the risk. Quadrant B
represents many internal operational risks such as check processing errors. The costs
are absorbed and the focus is on mitigation and prevention through improved
processing and training.
Type C events include concentrated exposures to high risk borrowers. These well
known risks are managed by constant management monitoring and control. Type D
events are frequently ignored due to a low frequency. Examples include many of the
structured finance products which represented short positions in an option. They
offered a long period of steady income punctuated with occasional large losses.
Cyclical risks are low-frequency-high-impact events characterized by their negative
skew and “fat-tailed” loss distributions. Investors incurring such risk can expect mainly
small positive events but are subject to a few cases of extreme loss. These risks are
difficult to understand. The difficulty stems from two factors. First, there is insufficient
data to determine meaningful probability distributions. In this case, the statistics are
descriptive, not predictive. Consequently, no amount of mathematics can tease out
certainty from uncertainty.1 Second, and perhaps more important, infrequency clouds
hazard perception. Risk estimates become anchored on recent events.
Overemphasis on recent events produces disaster myopia during a bull market, as
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instruments are priced without regard to the possibility of a crash. These facts lead to
risk mispricing and the procyclical nature of risk appetite.
Quantitative risk-management models are based on portfolio and option pricing theory
and provide a framework on how risk managers should act. These models build on
expected utility theory (EUT), which views individuals as expected utility maximizers. 2
Empirical support of EUT is mixed with numerous reported anomalies.3 Examples of
anomalies include holding losers, selling winners, excess trading, and herding.
An alternative, prospect theory,4 can explain these facts. Instead of being expected
utility (E(U)) maximizers, investors are viewed as expected regret (E(r)) minimizers
focusing more on loses than gains. This is reflected in Figure 2.
EUT focuses on wealth changes. The value function in prospect theory is based on
gains or losses relative to a reference point, usually par or the original purchase price.
Value function value
+
. Convex slope indicates pain of
loss (regret exceeds value of gain)
. The conflict between E(u) maximizing
and E(r) minimizing underlies many
anomalies
. Investment decisions involve 3 Rs:
return, risk and regret
Losses
Utility
Gain
Reference point
Figure 2
Investors Minimize Expected Regret
Market signals are complex. They include both information and noise. Information
concerns facts affecting fundamental values. Noise is a random blip erroneously
interpreted as a signal.5 Risk managers have developed shortcuts, rules of thumb, or
heuristics to process market signals. These belief-based heuristics incorporate biases
or cognitive constraints. These biases are the hidden risk in risk management, and
will now be investigated.
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A.
Regret
Risk is forward looking. Regret, however is backward looking. It focuses on
responsibility for what we could have done but did not do. Regret underlies several
biases. We try to minimize regret by seeking confirming data, suppressing
disconfirming information, and taking comfort that others made the same decision.
Consequently, regret can inhibit learning from past experiences.
Sunk costs are the first regret bias considered. Sunk-cost bias involves avoiding
recognizing a loss despite evidence the loss has already occurred and a further loss is
likely. Examples include the reluctance to sell impaired assets at reduced prices.
Usually this is defended as the market prices being too low. Most institutions,
however, reject the logical alternative of acquiring additional exposure at the market
price to exploit the alleged under pricing; thus, illustrating in this instance, price is of
secondary importance relative to regret.
Panic conditions are also based on a combination of regret and herding. In a crisis,
the reference is pessimism, and we actively seek bad news to confirm our belief. Thus
to minimize regret, we follow the herd not to be left behind and engage in panic selling.
This further depresses prices leading to continued forced selling and the creation of a
negative feedback loop as occurred in the fourth quarter of 2008.
Another regret-related bias is the house money effect. Risk managers will assume
greater risks when they are up in a bull market and lower risk in a bear market. Regret
is perceived to be less when risk of winnings is involved, than risk of initial capital.
This procyclical phenomenon leads to “buy high and sell low” behavior.
It illustrates the George Soros reflexivity or feedback principle, whereby markets affect
psychology and psychology affects markets. Positive feedback is self amplifying,
while negative feedback is self corrective. For example, collateral values rise during a
bull market. This increases their access to lower priced funding and liquidity, which
fuels further gains.
Finally, regret leads to confusing risk with wealth. Larger, better-capitalized financial
institutions can absorb more risk than smaller institutions. Their greater risk tolerance
lessens their downside sensitivity, especially during a bull market when income levels
are high. Thus, risk appetite increases with wealth. Risk and return are, however,
scale invariant. Larger institutions confuse the ability to absorb risk provided by capital
with the desirability of the risk position. Therefore, they acquire underpriced, higheryielding, higher-risk assets in bull markets.6 The JPM Chase London Whale situation
examined later is a recent example of this fact.
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B.
Overconfidence
Overconfidence occurs when we exaggerate our predictive skills and ignore the impact
of chance or outside circumstances. It results in an underestimation of outcome
variability.7 Overconfidence is reinforced by self-attribution and hindsight. Selfattribution involves internalizing success while externalizing failure. Structured finance
bankers and quantitative risk managers took credit for results during the boom, failing
to consider the impact of randomness and mean reversion creating an illusion of
control.8 Hindsight involves selective recall of confirming information to overestimate
their ability to predict the correct outcome, which inhibits learning. Disappointment and
surprise are characteristics of processes subject to overconfidence.
Industry and product experts are especially prone to overconfidence based on
knowledge and control illusions. Knowledge is frequently confused with familiarity.
This is reflected in the number of industry experts including most famously the former
Federal Chairman who missed the collapse of the housing and structured credit
bottom.9 This is due, in part, to misguided overreliance on quantitative credit scoring
models without understanding their limitations. Key model limitations include the
following:

Homogenous populations: Statistical models require large homogenous
populations with a long history of observations. The new structured
finance credit portfolios were small, heterogeneous, and concentrated
with limited histories.

Statistical Loss Distribution: Loss distributions for credit are skewed, with
unexpected event losses hidden in the distribution’s fat tails. Models tend
to be blinded by the mean and underestimate extreme events.
Historical basis: History is a guide, not the answer. The past represents
but one possible outcome from an event sequence and is not an
independent observation. History becomes less relevant as markets and
underwriting practices change. This was especially true for mortgage
default models. They ignored the impact of securitization of mortgage
originator underwriting practices.10


Uncertainty: Decisions involve both risk, known unknowns, and
uncertainty, unknown unknowns, elements. Financial models adequately
contemplate the former but inadequately deal with the latter. Managing
uncertainty requires judgment, not calculation.
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Control reflects the unfounded belief of our ability to influence or structure around risk.
Risk is accepted because we believe we can escape its consequences due to our
ability to control it. Examples include the perceived ability to distribute or hedge risk,
independent of the likelihood of being better or faster at identifying risk than the
market.
This reflects an optimistic underestimate of costs while overestimating gains.
Optimism is heightened by anchoring when disproportionate weight is given to the first
information received. This is usually based on the original plan, which tends to
support the transaction.
Time-delayed consequences magnify overconfidence as individuals weigh short-term
performance at a higher level than longer-term consequences. These occur whenever
short-term benefits clash with long-term effects. Although we know of the potential
negative long-term effects, we believe that they will not happen to us, at least during
the current accounting period.
C.
Statistical
Statistical bias involves confusing beliefs for probability and skill for chance by
selecting evidence in accordance with our expectations11. Economics is a social
science based on human behavior. Prices are not determined by random number
machines12. Rather, they come from trades by real people. Feedback loops, prices,
trades and people complicate statistical modeling, and invalidate the use of normal
distributions as used in the physical sciences.
Institutions find it difficult to accept chance and are frequently fooled by randomness.
A manifestation is the representative bias, whereby we see patterns in random events.
We interpret short-term success as “hot hands” by a skilled trader or banker. Riskadjusted return on capital and other measures are unable to distinguish results based
on luck versus skill.
Statistically based risk management practices are inherently limited. They are unable
to reflect the hidden risk that the state of the world may change rendering current state
data obsolete. For example, switching from a boom to a bust cycle impacts
correlations. Formerly diversified positions begin moving together, triggering
unexpected losses. They are unexpected because such movements are unfamiliar.
We tend to view the unfamiliar as improbable, and the improbable is frequently
ignored.
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Actions and outcomes can be unrelated. Consequently, it becomes important to
examine the decision process and not just the outcome.13 As Scholes notes, to value
risk or price reserves you must reflect the values of the options not purchased to
hedge the position. Since this is not priced, it creates incorrect capital allocation
incentives.14 Thus, the “lucky fool” is rewarded and encouraged with bonuses and
increased capital until luck turns and losses are incurred. Examples include the
numerous apparently lucky real estate experts at institutions like Bear Stearns and
Lehman. Eventually, all lucky streaks come to an end as this one did during 2008.
Another statistical error prevalent during a boom is extrapolation bias. This occurs
when current events or trends are assumed to continue into the foreseeable future,
independent of historical experience, sample size or mean reversion. Undoubtedly,
this resulted in many of the projections underlying structured credit proposals. The
major error focused on the belief that housing prices would not decline nationwide in
the US.
Perhaps the most dangerous statistical bias is disaster myopia. This occurs whenever
low-frequency but high-impact events are underestimated. Since the subjective
probability of an event depends on recent experience, expectations of low-frequency
events, like a market or firm collapse, are very small. These types of events are
ignored or deemed impossible, particularly when recent occurrences are lacking. This
causes a false sense of security as risk is underestimated, or assumed away, and
capital is misallocated. Unlikely events are neither impossible or remote. In fact,
unlikely events are likely to occur because there are so many unlikely events that can
occur.15 Thus, the longer the time period, the higher the likelihood of a “Black Swan”
event occurring.16
D.
Herding
The previous discussion concerned individual psychological aspects of risk decision
making. There are also social aspects to decision making when individuals are
influenced by the decisions of others as reflected in herding and ‘group think’.
Herding occurs when a group of individuals mimic the decisions of others. Through
herding, individuals avoid falling behind and looking bad if they pursue an alternative
action. It is based on the social pressure to conform, and reflects safety by hiding in
the crowd.17 In so doing, you can blame any failing on the collective action and
maintain your reputation and job. Even though you recognize market risk, it pays to
follow the crowd. Managers learn to manage career risk by clinging to an index.
Essentially, principal loss is converted into benchmark risk.
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Herding reduces regret by rationalizing that you did no worse than your peers. It
constrains both envy during an upswing and panic in a down market. This is critical in
banking when performance contracts are based on relative performance measured
tied to peer groups.18 Herding underlies why banking experts’ forecasting abilities are
poor. The experts tend to play it safe by staying close to the crowd and extrapolating
past performance.19
A related effect is an informational cascade. A cascade is a series of self-reinforcing
signals obtained from the direct observation of others. Individuals perceive these
signals as information even though they may be reacting to noise. This is referred to
as a positive feedback loop or momentum investing, which can produce short-term
self-fulfilling prophecies.
Herding amplifies credit cycle effects, as decisions become more uniform. The cycle
begins with a credit expansion leading to an asset price increase. Investors rush in to
avoid being left behind using rising asset values to support even more credit. This
explains why bankers continued risk practices even though they feared this was
unsustainable and leading to a crisis. Eventually, an event occurs which triggers an
asset price decline. This causes losses, a decline in credit, and an exit of investors,
which strains market liquidity.
E.
Group Think
Group think, or organizational pressure, enhances cognitive biases. It occurs when
individuals identify with the organization and uncritically accept its actions. Once the
commitment is made, inconsistent information is suppressed. Consequently, mutually
reinforcing individual biases and unrealistic views are validated.20
Experts are prone to group think. They tend to limit information from all but other
expert sources. Thus, they repeat statements until they become accepted dogma
regardless of their validity, due to a lack of critical thinking.
The subprime collapse illustrates this fact. The industry participants used the same
consultants and models for their projections. The consultants based their reports and
recommendations on the surveys of industry participants. Once the perception of a
bull market took hold, it was reinforced and accepted uncritically. When the crash
occurred, the experts were taken by surprise by a supposed perfect storm.
This is illustrated in the 2006 Business Week cover story in which risk officers at
numerous institutions, including Bear Stearns and Lehman, are surveyed.21 They
11
believed that despite the risks taken they were safer than ever. This belief was based
on complex risk models and market diversification. The faith in risk management
encouraged institutions to increase their risk exposures, believing they were under
control.
F.
Sentiment Risk
The aggregate investor error based on biases is sentiment risk. It can be either
optimistic or pessimistic and is time varying.
Sentiment risk is zero in an efficient market. Paul Samuelson noted markets in the
short-term can be micro efficient concerning individual instruments, but macro
inefficient regarding the market as a whole. Additionally, during the short-term the
direction of the inefficiency is likely to widen due to momentum and herding.22 Most
risk models ignored sentiment risk. This causes losses when sentiment changes
leading to closed markets and mark to market losses like those at MF Global and JP
Morgan Chase’s London Whale.
During a late stage boom with high sentiment levels, behavioral risk factors will
dominate and quantitative risk measures will be unreliable. This is reflected in the
famous comment, “As long as the music is playing, you have to get up and dance”.
This is characterized as irrational exuberance where prices are driven principally by
momentum and herding reflected in high liquidity levels. When sentiment is low,
fundamentals will rule. Prices may, however, diverge from fundamentals.23
Recognizing and dealing with biases is complicated by three factors. First, bias can
be amplified within organizations due to incentive misalignment and group think. Next,
we diminish information inconsistent with our existing views, while searching for
conforming information. Finally, this leads to a false sense of security and reduced
vigilance.
IV
Pre Crisis Environment
The financial services industry suffered from over capacity and product
commoditization, which has pressured margins. Institutions increased risk exposure to
enhance nominal returns without increasing shareholder value as reflected in Figure 3.
Figure 3 illustrates that not all risk increases enhance shareholder value.
Opportunities to achieve true and lasting alpha like returns, “D”, are difficult to find in
the highly competitive financial services industry. Entry barriers are low and
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substitutes abound. Consequently, most risk increases involve systematic market, or
beta risk which shareholders can achieve on their own. Distinguishing between beta
and alpha performance can be difficult.
Figure 3
Value Implications of Risk Appetite Changes
This difficulty is especially true for new products with limited historical data. A strong
and experienced governance system is needed to avoid paying alpha bonuses for
beta returns. Movements along the curve represent changes in firm risk appetite.
Changes in risk appetite have direct impact on capital requirements to maintain total
risk levels.
Risk exposures can be increased on both sides of the balance sheets. Asset risk is
increased by taking tail, downside, risk exposure inherent in many of the new products
with option like payoffs. For example, Merrill Lynch’s one-day VAR increased by
almost 5 times from 2001 through 2007. 24
Although VAR has its problems as a precise risk indicator, as a trend indicator it is
useful. On the liabilities side of the balance sheet, leverage levels increased
dramatically. This was accomplished by the large-scale use of off-balance sheet
vehicles at banks and by raising debt to capital level at broker dealers.25 In fact, the
large scale post crisis capital raises by many institutions served as a proxy for the pre
crisis undercapitalization. In Merrill Lynch’s case, that totaled almost $32 billion in the
first half of 2008.26 The consolidation of off-balance sheet vehicles by banks that were
13
triggered once liquidity evaporated added billions of risk assets to already strained
balance sheets.
Flawed risk models contributed to the problem. Overconfidence in the models created
an illusion of control. Profits were rising and the risk models did not indicate any
undue concern. The models, however, failed in several respects. First, they
mischaracterized the nature of risk by assuming risk to be exogenous to the system.
Risk, however, is endogenous to markets caused by participant interactions.
Consequently, market behavioral changes were ignored or not adequately modeled.
Next, model risk is heavily dependent on data frequency and availability. Thus, for
new products with a limited history, the models were inadequate. Finally, even if you
have the data, models are based on experience, not exposures. Just because
something has not occurred yet, the exposure may still exist. This is particularly true
when dealing with large-scale event risks or “Black Swans”. Risk models concentrated
on the ordinary to the exclusion of infrequent extraordinary tail events by confusing
history with science. This increased the incentives to take excessive remote risk
based on overconfidence in the stability of observed patterns.27
Regulators compounded the problem by legitimizing the models. Basel II allowed
institutions to rely on their own internal risk models to set capital levels without
realizing the incentive for institutions to underestimate risk.28 Furthermore, regulators
increasingly relied on agency ratings. The agencies were using the same flawed
models as the firms whose products they rated.
Decisions must be based on possibilities, not just history. History is just one possible
scenario. Thus, not all risks are visible in historical returns. This is the basis of the
peso problem where the extra yield, supposedly alpha, is merely compensation for an
unseen risk, which may occur regardless of whether it has occurred in the past.29
The September 2008 collapse of independent investment banks illustrates the use of
increased risk to compensate for a declining business model. Independent investment
banks were largely artificial creations resulting from the Glass-Steagall separation of
commercial and investment banking activities. They enjoyed a profitable existence up
to the 1976 elimination of fixed commissions on stock trades. They then began
searching for alternative replacement revenue sources. Many, like Salomon Brothers,
moved into higher risk - higher return activities like proprietary trading. The 1998
repeal of Glass-Steagall allowed commercial banks to enter agent-based underwriting
and advisory businesses. This repeal had a predictable negative impact on
investment banks.
14
Investment banks, once again, began searching for higher margin activities. This was
clearly stated in the 2005 Goldman Sachs annual report. The business model,
subsequently known as the “Goldman Model”, noted their traditional agency business
had become a commodity. They now had to combine capital with advice. Goldman
began moving into private equity, trading, and investing in structured products. Its
initial success with this model caused considerable envy among its competitors who
began copying the model.
The Goldman Model was essentially an asset-heavy long only activity. It involved a
variant of the carry trade or 5L strategy. The 5Ls are:
1.
2.
3.
4.
5.
Long term investments.
Large concentrated holdings.
Low-quality high-risk assets.
Leveraged positions.
(I) lliquid assets with funding mismatch
The model worked in a bull market awash with liquidity and declining interest rates.
The model also contained a fatal flaw. The assets were funded short term, primarily in
the overnight repo market. Thus, they used a toxic combination of high 30:1 leverage
and short term funding. Any change in the macro economic environment causing
investors to change their risk appetite would cause liquidity challenges - just as in
Long Term Capital Management (LTCM) and again with MF Global. Investment
banking risk management failed in two key areas. First, they held insufficient capital to
withstand the inevitable losses from holding higher risk assets. Second, they
compounded the error by having inadequate liquidity to cover creditor concerns once
portfolio losses began occurring.
Failure of the Board to recognize and remedy the situation represents a governance
breakdown. Frequently, directors were unaware of the risk implications of strategic
initiatives, and confused short-term results with skill. For example, Merrill Lynch’s
strategy to match Goldman Sachs and become the structured finance market share
leader required assuming billions of additional warehouse asset risk. Essentially, they
were making a franchise bet. This involved a large increase in risk appetite without
adequate consideration of negative scenarios or capital structure implications. Next,
incentive arrangements produced counterproductive behavioral changes. Strong
managers began exploiting weak governance. Incentives became short term oriented
and based on nominal income with insufficient risk adjustments. Risk manager
concerns, if raised at all, were presumably ignored or overruled. Especially, because
the models, ratings and regulators indicated risk was under control. 30
15
Even within risk management, organizational impediments exist. Individual risk
functions tend to operate as independent “silos” with little or no strategic connection.31
Additionally, there is limited consideration of business models and market states when
evaluating transaction risks. Market state changes are caused when an unstable
market undergoes a rapid regime change. Herding causes the formation of “super
portfolios” of overlapping positions. Once these positions reach a critical stage, a
random trigger causes the unwinding of positions. Correlations change, diversification
breaks down, and losses occur over formerly diverse asset classes.32
Strategic risk is the possibility of an event that impacts an organization’s ability to
achieve its business plan was ignored. The integration of risk into strategic planning,
capital management, and performance measurement is needed. 33 This would
combine business and risk considerations into a single, whole-firm view of value
creation under an integrated risk strategy framework.
V
Risk Strategy Framework
The purpose of risk management should be to enhance an institution’s value. Thus,
its focus is as much strategic as transactional. The key is to ensure the institution has
sufficient capital to fund its strategic agenda. It involves using preloss active risk
management and post loss capital management. In effect, risk management is a
capital substitute. Viewed in this light, risk management becomes part of the overall
finance function and not an isolated specialist activity.
Value is created on the asset side of the balance sheet through investment decisions.
The value of risk management is to ensure funding of the investment plan by
maintaining capital market access under all conditions.34 This entails maintaining a
total risk profile consistent with rating targets. Consequently, balancing asset portfolio
risk with capital structure is required. Failure to do so can undermine an institution’s
strategic position and independence.
Questionable strategic growth initiatives that were inappropriately funded underlie the
problems at many financial institutions.35 Bankers believed that growth added value.
Unfortunately, growth can destroy value when the returns are less than their cost of
capital. This is illustrated here:
16
Value = Cash flow + [Investment (Return on Assets – Cost of Capital) T ÷ Cost of Capital]
Cost of Capital
(Term A)
(Term B)
Source: adapted from F. Modigliani and Term B M. Miller, Dividend Policy, Growth
the Valuation of Shares; Journal of Business 34 (October, 1961) 411-433.
and
Term A represents the value created by assets already in place, while term B is the
value created by growth. T, the competitive advantage period, represents the number
of years the firm enjoys the opportunity to invest in profitable projects. Growth can
destroy value when an institution invests in projects earning less than their cost of
capital. Value creation can also be impacted through poor risk management, which
causes the disruption of a firm’s investment program due to inadequate capital and
liquidity positions to absorb unexpected events.
Insufficient returns from growth initiatives can strain capital structures and dividends.
Maintaining such growth, absent a dividend cut, requires either a dilutive equity
issuance or increased leverage. Rather than potentially upsetting shareholders, many
institutions chose to increase leverage levels as reflected in Figure 4.
Types of Risk
_________________________________________________
1Q04
1Q07
_________________________________________________________________________
Bear Stearns
28
34
Morgan Stanley
25
34
Lehman Bros.
25
32
Merrill Lynch
19
28
Goldman Sachs
20
28
______________________________________________________
Source: SEC filings and Kara Scannell, “SEC faulted for missing red
flags at Bear Stearns,” Wall Street Journal, September 27, 2008, A3
Figure 4
Gross Leverage Levels (Total assets divided by total shareholders’ equity)
Risk management includes a capital structure decision process linking strategy and
capital levels. Firms can change the nature of risks retained by using risk
management.
As Figure 5 highlights, the cash-flow volatility of current and future investments
combined with the strategic investment plan drives the value of risk management.
Low volatility, low-growth firms with limited investment needs have lower risk
17
management needs than rapidly growing firms. Financial institutions have an
additional demand for flexibility reflected in high investment-grade rating targets. This
is due to their liability sensitivity. Their customers are also creditors concerned with
deposit and trading products. Thus, such ratings are necessary to maintain
customers.
Risk Management
Business Model and
Effect on Cash
Flow Volatility
Corporate Strategy
and
Investment Plan
Ratings Targets
Industry Needs to Achieve
Investment Grade
Risk Management Strategy
Figure 5
Drivers of Risk Management Strategy
Traditional underwriting, mitigation and transfer risk management techniques can be
used to select those risks that the institution is competitively advantaged to own and
eliminate the rest. For example, community banks have an informational advantage
regarding local clients. Thus, they should retain such risk up to prudent concentration
levels. Alternatively, market risks, like interest rate risk should not be held unless the
institution possesses special information, skill or they are perceived to be mispriced.
The retained risk should be covered by capital consistent with a ratings goal to ensure
capital market access sufficient to fund the investment plan. Viewed in this light, risk
management and capital can be seen as interchangeable with capital being the cost of
retained risk. In fact, risk management is essentially tax-deductible synthetic equity.
The key is to avoid a mismatch between the assets and liabilities and equity of the
balance sheet. Too little capital relative to asset risk reduces flexibility, while excess
capital depresses returns.
The overall institutional risk level is dependent on the board’s risk appetite – the level
of risk the organization is willing to assume on both sides of their balance sheet in
pursuit of its strategy. Risk appetite is a relative term among stakeholders. Usually
aligned, there are instances when management and stakeholder appetites differ.
Management’s risk appetite is best expressed as a continuum reflected in Figure 6.
18
Risk Appetite Continuum
Figure 6
Profit / Loss Distribution
Probability
Profit Warning – National City
Rating Watch – Fifth Third
Dividend Cut – Citi
Downgrade – Morgan Stanley
Raise Capital - Merrill
Management Replaced – Prince, O’Neil
Regulatory Action – Cease and Desist
Memorandum of Understanding
_____ Failure – Bear, Lehman, WaMu
_____
_____
_____
_____
_____
_____
_____
-
0
+
Profitability
Figure 6
Risk Appetite Continuum
Adapted from Oliver Wyman, “The New Finance and Risk Agenda: What is Your Risk Appetite?” 2008.
Obviously, no one consciously plans on accepting the risk of replacement, regulatory
action, or failure. Rather, these situations result from the failure to consider
adequately the probability of ruin in rare bad states. These strategies involve bets
against randomness and an acceptance of peso risk. The 20-plus year financial bull
market during the great moderation lulled management, directors, regulators, and
shareholders into a false sense of security. They simply ignored these rare but
possible negative states by assuming large risk positions relative to their capital. 36
Risk strategies that are successful except for rare events are like having an airbag that
works except when there is a crash.
Risk appetite decisions involve determining how much of the firm’s value is at risk
should the worst case materialize, whether this is tolerable, and if not, how much
additional capital is needed to self insure. Figure 7 illustrates the risk management
linkages among various stakeholders, which needs to be reconciled by the firm’s
board of directors. The skewed compensation systems allowed managers to exit with
huge payouts, and keep prior year bonuses, represents a board failure. It encourages
managers to “roll the dice” in a “heads I win/tails you lose” situation. Senior
management’s interests were misaligned by their compensation systems.
19
Consequently, they acted in a predictable and rational manner at the expense of their
stakeholders.
Risk Management
Figure 7
Risk Appetite and Value Creation
Unresolved conflicts between internal and external risk appetite have underlined
problems at many institutions. Management had undertaken new higher risk strategies
with capital structures incapable of absorbing the inevitable losses in pursuit of
maximizing their bonuses. Complicating matters is the procylical nature of risk
appetite. As a bull market ages, income increases and vigilance declines. Institutions
extrapolate, and assume favorable short-term trends will continue. Eventually, absent
strong governance, they move further out on the risk curve by confusing a bull market
with skill. This results in an over exposed position once a correction occurs.
Risk models contributed to increasing risk appetite. Individuals chose to maintain a
given level of risk. Perceived risk declines trigger behavioral changes as we increase
our risk exposure to return to our original risk level. Institutions mistakenly believed
risk management had reduced risk, and compensated by increasing their risk
exposures.37 This leads to the paradoxical conclusion that risk mitigation does not
reduce risk – rather it redistributes it unless risk appetite is reduced by changing
incentives.
20
Additionally, many financial firms held large amounts of risk in which they had limited
competitive advantages. They had effectively shifted from an “originate to distribute”
to an “originate to hold” business model. This market risk, beta, while increasing
nominal income, failed to create shareholder value. It involves large, leveraged, and
illiquid concentration bets in tail risk options like assets such as structured products,
based on models that underestimated risk38 to exploit “blind spot” weaknesses in risk
management systems. Structured finance products are the perfect moral hazard
products to exploit the risk and compensation systems.39 The legitimacy of structured
products was enhanced by the high, often AAA ratings awarded to such products,
which provided the appearance of liquidity.40
It is important to distinguish liquidity from solvency. Liquidity concerns the composition
of the balance sheet. Specifically, it focuses on having enough cash to withstand a
run of bad events. Liquidity allows you to survive long enough to succeed. Solvency
relates to the overall collateralization of liabilities with asset values.
In a market crisis state, the key concern is liquidity. Yet surprisingly, both the
regulators in BIS II and the rating agencies had expressed little concern on this issue.
Asset prices become volatile during a liquidity crisis. Again, this was highlighted in
LTCM and MF Global. Their trades eventually worked, but since they had insufficient
liquidity, they were forced out before they could realize the gains.
The size of the bid / offer spread during the panic stage complicates the conversion of
assets into cash without loss. The inability to convert long-term assets to cash to
match short-term debt maturities, caused firms like MF Global, Lehman Brothers and
Bear Stearns to fail even though they were arguably solvent.
There are two sources of liquidity. Traditionally, institutions held cash or cash-like
liquidity buffers to cover asset price liquidity concerns. This is, however, expensive.
Many institutions switched to liability-based liquidity. This was based on the ability to
have debt access on reasonable terms. Investment banks typically used short-term,
frequently overnight funding to support long-term asset positions because it was less
expensive. Unfortunately, this availability is fragile and subject to potentially volatile
market conditions.41 The presumption of the ability to borrow is state-specific. It holds
during normal periods, but is invalid during panic states when price declines generate
more sellers than buyers, thus creating a liquidity black hole.42 Credit based liquidity
is illusory. The combination of leverage without liquidity is deadly regardless of the
quality of a firm’s assets. Asset problems eventually impact a firm’s ability to access
funding, which leads to a liquidity crisis.
21
VI
GOVERNANCE
A key, but often neglected, component of risk management is governance. As Rene
Stulz rightfully points out, risk managers are not solely responsible for the current
credit crisis. At its core, risk management is an exposure measurement and
accounting system. The decision to take major risks is the responsibility of top
management and the board of directors.43 Nonetheless, risk management provided
justification for the taking of those risks.
Governance involves designing appropriate incentives and controls to ensure the
alignment of potentially conflicting management and shareholder interests. This
involves assigning decision rights, establishing performance metrics, and developing
an appropriate rewards system. This is especially important to financial institutions
that take opaque risk positions, which do not manifest themselves until later. Under
these circumstances, high powered incentive compensation arrangements coupled
with information asymmetry create an incentive for management to game the system
leading to Decisions at Risk (DAR)44 in Figure 8. Bonuses tied to short-term
performance and equity options misalign management and shareholder interests
resulting in excessive risk taking.
Asymmetric Information
Behavioral Bias
Adverse Selection – Lack
Information and Chose
Incorrectly
Optimism
Overconfident
Illusion of Control
Moral Hazard – Lack Information
on Performance
Figure 8
Control
DAR
Internal
Board Monitoring
Incentives
Sanctions
External
Regulators
Market for Corporate Control
DAR Control Framework
Management can exploit its information advantage to deceive the Board of Directors.
Structured products have a high DAR because they involve complex accounting and
valuation problems. This was the reason underlying Warren Buffet’s charge that they
constituted “weapons of mass destruction”.45 Furthermore, management may lack
the capability to oversee and understand their risk positions. In these cases, senior
management becomes a captive of middle managers whose incentives are to
maximize their bonuses through increased risk taking. Arguably, this occurred at Bear
22
Stearns and JP Morgan Chase (2012) where senior management did not understand
its risk exposures.
Boards, suffering from DAR problems, became co-opted by management. They
seldom questioned management unless forced by a market crisis. Symptoms of
ineffective boards include:
 Large boards
 Inexperienced directors
 Retired CEOs predisposed to side with the CEO
 Limited ownership: this curtails their commitment
Boards need to understand the institution’s strategy, risk appetite and the impact of
business plan assumptions. Otherwise, they will fail to notice risk appetite changes,
the risk implication of strategy changes, required capital levels, and the incentive
impact of compensation schemes and franchise bets. Unfortunately, attempts to
improve Board performance can face challenges. This is similar to regulatory capture
when mechanisms created to protect individuals end up acting in the interests of the
regulated firms.
Internal control breakdowns usually lead to declining performance and shareholder
pressure and changes in corporate control. The usual form of these actions involves
proxy battles and hostile takeovers. In regulated industries, like banking, regulations
make such actions difficult. The regulators become a replacement for the external
market for control. Regulators are, however, an inefficient replacement. They are not
necessarily aligned with shareholders, and face the same DAR problems as the board
of directors. Furthermore, they are subject to being co-opted. The answer is not
necessarily more regulation, but allowing for increased market discipline, which can be
achieved in two areas.
First, large active shareholders with board representation, such as private equity firms
can counterbalance management. Unfortunately, bank holding company rules
complicate this effort. An alternative is based on contingent capital provided by private
insurers in meaningful amounts. The insurer will have a monetary incentive to
challenge management and ensure appropriate risk management oversight. Another
quasi-market approach is the requirement of banks to issue subordinated debt.
Subordinated debt would act as the “canary in the coal mine” to provide an early
warning of bank solvency issues. We can expect further developments in this area.
Absent such solutions, banks will suffer an information uncertainty discount, which will
raise their cost of capital. Thus, an institution’s ownership structure and composition
should be an important risk management consideration.
23
VII
NEW DIRECTIONS
We need to move beyond risk measurement to risk management that integrates risk
into strategic planning, capital management and governance. Enterprise risk
management provides a framework to integrate these functions.
Enterprise Risk Management (ERM): The First Step 46
Risk management is a strategy and a means to an end, and not an end in itself. The
focus is on linking the control aspects of governance with strategy and performance in
an integrated fashion. Risk is viewed on a total firm portfolio basis linking both sides of
the balance sheet. The firm, and consequently, risk management is more than the
sum of the parts. The interactions among various units and risks, something ignored
by silo-based risk management, is just as important as the units and risks themselves.
ERM provides such a unifying mechanism. Its scope goes beyond traditional financial
risks to include human resources, incentives, and governance matters as well.
ERM is a consolidated top-down cross-functional total risk management exercise
which cuts across all business units and risk types. The focus is strategic, not
transactional. It seeks to improve decision making through a portfolio view of
interrelated risks across the firm. This is accomplished by imbedding a risk culture
within business units so risk considerations become an input versus a consequence of
these strategies. This ensures that an organization in control, rather than a control
organization, develops. This is especially important in a rapidly evolving financial
services market with institutions struggling with declining core operations, and
searching for replacement business models.
Risk management does not operate in a vacuum. It is context-dependent, and must
take the external environment into account. ERM, can become too inward-looking,
and fail to consider the firm’s adaptability to changing unstable market conditions. A
useful approach is referenced in Figure 9.
24
Figure 9
Firm and Its Environment
Industries are interactively complex. The relationships are nonlinear, meaning that
small changes can have disproportionate impacts. Additionally, the system is tightly
connected by feedback loops. Events spread quickly throughout the system in
unpredictable ways. The current crisis represents a system failure and attempts to
identify a single cause or assign blame are fruitless.
To ensure success, risk strategies must be flexible enough to change with evolving
environmental conditions. Sophisticated systems that work only in one market state,
that is, the current one, are of limited use in alternative states. Firms need enough
resiliency to survive and adapt to unanticipated environmental changes.
Enterprise Resilience (ER): The Next Step?
Firms are part of a complex living market system. Crises within that system may be
infrequent, but are inevitable. A firm’s ability to adapt to unforeseen events – its
resilience – becomes a critical success factor. The system is too complex to predict
when and where accidents will occur. The key is the flexibility to sense and respond to
accidents. ER is a possible next step in the development of risk management as
reflected in Figure 10.47
25
Types of Risk
Figure 10
Adaptive Risk Management
ER involves a focus on what can happen regardless of probability, and across
multiple market states. Then the firm needs to build a risk management structure to
withstand whatever category market storm fits its risk tolerance. Although not optimal
in all market states, ER ensures survival over multiple market states.48
VIII
Post Crisis Reminders: MF Global and JPM Chase
The MF Global and JPM Chase incidents within just a few years from the crisis is a
reminder of the unfinished nature of rethinking Risk Management. Fortunately, they
occurred in relatively benign markets. Thus, they were isolated incidents, like LongTerm Capital Management, and did not trigger a market collapse.
MF Global (2010 / 2011)49
Jon Crozine joined MF Global, a struggling futures broker, in 2010. He attempted to
transform from an agency business model executing customer transactions toward a
5L principal trading model similar to his previous firm, Goldman Sachs. He focused on
acquiring large positions in “mispriced” European sovereign debt in the summer of
2010 starting with an initial $1B limit, which he personally administered. He used a
repurchase to maturity financing arrangement to fund the investments. This treated
the debt investments as a sale with a forward purchase agreement. This allowed MF
26
Global to book the investment income while keeping the assets off balance sheet.
Thus, mark-to-market income statement volatility was minimized. Additionally, Crozine
implemented a new performance driven incentive system and made numerous
personnel changes.
The arrangement, however, exposed MF Global to heightened liquidity risk from
margin calls should it or the investments suffer downgrades. The program was
increased to almost $7.5B in August, 2011. This represented almost 15% of its assets
and over 4.5 times its equity far exceeding its peers. Concerns raised by the firm’s
risk officer resulted in his replacement. Questions by the board prompted Crozine to
threaten resignation. Unfortunately, a Euro crisis occurred in the summer of 2011.
This triggered a massive margin call, which MF Global could not meet, along with
ratings downgrade actions. The firm failed in the fall of 2011.
JPM Chase (2011 / 2012)50
JPM Chase, like many banks, operated in a difficult environment. Loan growth was
weak, high excess deposits and low interest rates depressed margins. A centralized
chief investment office (CIO) was to help manage this situation under Ira Drew. Bruno
Iksil joined the group in 2005 in London. His “hedges” produced profits of $2.5B by
2011. Both Iksil and Drew received multi-million dollar incentive contracts. Senior
management, emboldened by the performance and JPM Chase past risk management
success encouraged more CIO profits. Faith in risk management motivates bankers to
take more risk than they would otherwise assume because they believe they are in
control. Their portfolio went from $51B in late 2011 to over $157B in February, 2012.
The trades involved hard to value illiquid over-the-counter credit derivatives. The
assets were deemed low risk. Consequently, their risk weighted assets requirement
was low. The CIO morphed from hedging into long only 5L positions. The bets
became so large that they moved the market and became known as the London
Whale. Thus, when conditions turned, and losses occurred, the CIO was unable to
unwind. The result was a $6.2B loss in the spring of 2012. The U.S. Justice
Department indicted Iksil’s direct boss, Martin-Artajo, and Julien Grout, who worked for
Iksil in August, 2013. They are charged not for the loss, but for understating its size.
IX
Conclusion
The financial crisis, the collapse of MF Global and the JPM Chase London Whale,
illustrate the shortcomings of current risk management. Risk management lagged
financial innovation. Risk at best, is measured, but not managed adequately. Instead,
it evolved as a ritualistic prediction activity. Conventional risk management became
27
overconfident, a regulatory fiction behind which excessive risk taking occurred. Thus,
it is time to Rethink Risk Management Again.51
Some risk management supporters allege the problem is not with risk management
itself. Rather, they believe it was misapplied and ignored by management. This is
difficult to accept given the active role risk management played in many of the risk
failures.
Risk management must include the risk return tradeoff facing the entire firm. This
includes strategic risk and capital structure issues. There is nothing necessarily wrong
about high risk strategies, provided the firm is compensated, understands the risk, can
withstand an adverse event, and stakeholder interests are aligned.
The risk from declining banking business models increases concerns for misalignment.
ERM and ER offer the opportunity to bridge this gap by combining business and risk
considerations into a single, whole-firm view of value creation over multiple market
states. Next, governance issues, which are partly the source of the current problems,
must be addressed. Governance concerns the assignment of decision rights to
identifying, addressing, and resolving conflicting stakeholder claims. Additionally,
reporting transparency that reflects risk appetite and the risk profile is needed. The
most important component of risk management is management, not measurement. If
successful, these developments will transform risk management into a strategic value
enabler.
NOTES
1
This is the Knightian distinction between risk, randomness with knowable probabilities and
uncertainty, randomness with unknowable probabilities. See F. Knight 1921, Risk, Uncertainty
and Profit, Houghton Mifflin, 1921.
M. Friedman and L. Savage, 1948. “The Utility Analysis of Choices Involving Risk,” Journal
of Political Economy.
2
D. Ellsberg, 1961, “Risk, Ambiguity and the Savage Axioms,” Quarterly Journal of
Economics 643.
3
A. Tversky and D. Kahmeman, 1992, “Advances in Prospect Theory; Cumulative
Representation of Uncertainty,” Journal of Risk and Uncertainty, which builds on their earlier
work. Prospect theory is a key component of Behavioral Economics. Behavioral finance is a
subset of Behavioral Economics, applying its concepts to asset pricing. This article uses the
terms interchangeably.
4
28
5
See E. Black 1986, “Noise,” Journal of Finance, July.
6
This is consistent with the H. Minsky financial instability hypothesis. Investors increase their
risk exposures driving bull markets until they have taken on too much. See H. Minsky, 2008,
Stabilizing an Unstable Economy, McGraw-Hill.
7
This is magnified by the naïve use of market-based risk-management tools.
8
Studies indicated the underestimate at 15% - 25%. The direction of the overconfidence is
usually positive reflecting a related optimism bias.
9
Inappropriately designed incentive compensation reinforces overconfidence.
U. Rajan, A. Seru, V. Vig, “The Failure of Models that Predict Models: Distance, Incentives
and Defaults,” Working Paper, September, 2008.
10
See P. Bernstein, 1996, “The New Religion of Risk Management,” Harvard Business
Review (March-April).
11
12
W. Sharpe, 2007, Investors and Markets, Princeton University Press 11.
13
P. Rosenweig, The Halo Effect, Free Press, 2007.
14
M. Scholes, “Crisis and Risk Management,” AEA Papers and Proceedings, May, 2000.
15
P. Bak, 1996, How Nature Works, Springer-Verlay.
Black swans are high impact unexpected rare events. The term was popularized by N.
Taleb in The Black Swan: The Impact of the Highly Improbably (Random House, 2007).
16
This is reflected in Keynes’ statement that it is better for a banker’s reputation to fail
conventionally, than to succeed unconventionally.
17
18
The industry expert impact is significant, as most large financial institutions adopted best
practices based on similar experts.
Relative performance measures are a form of sophisticated “me-too” metrics. Rather than
focus on absolute value creation, they focus on arbitrary market silos that may be in a
downturn.
19
See J. Chevalier and G. Ellison, 1997, “Risk Taking by Mutual Funds as a Response to
Incentives,” Journal of Political Economy.
20
E. Thornton, D. Henry and A. Carter, 2006, “Inside Wall Street’s Culture of Risk,” Business
Week (June 12).
21
29
22
H. Shefrin, 2008, Ending the Management Illusion, McGraw-Hill.
23
See T. Debels, Behavioral Finance (Garant Uitgevers, 2006) 183 for a discussion of various
forms of behavioral finances that can occur in markets.
24
Credit Suisse, “European Banks”, June 22, 2008.
25
Common off-balance sheet vehicles included, among other things, structured investment
vehicles, and asset-backed commercial paper conduits. They functioned as de facto
unregulated banks developed to arbitrage banking regulation.
26
Lehman Brothers Increased its asset size by almost $400 billion in 2004-2007, on only a $6
billion capital increase. At the time of its bankruptcy, leverage levels exceeded 30 to 1. 2004
was the year in which the SEC enacted a new capital rule allowing major broker dealers to
increase leverage levels based on internal risk models.
Sometimes known as “the Law of Small Numbers”; in other words the exaggerated belief
that a small sample resembles the population from which it is drawn. See M. Rabin,
“Inferences by Believers in the Law of Small Numbers.”, Quarterly Journal of Economics,
2000.
27
28
Warren Buffett referred to this a a self-graded exam. Berkshire Hathaway 2002 Annual
Report.
29
Peso risk refers to the possibility an unprecedented or infrequent event affects asset prices.
The extra, alpha, yield is an illusion based on the small sample size bias in expected returns
defined here for the first time.
30
This reflects the fundamental asymmetry in rewards between prevention and rescue. This
was highlighted by large compensation awards granted to postcrisis risk managers brought in
to rescue institutions like Merrill.
This is highlighted by the statement from Citigroup’s CFO, Gary Crittenden, in October,
2007. He stated they thought the risk in structured products was predominately market risk,
when in fact, it was credit. Thus, they missed the real risk in their portfolio.
31
32
This was demonstrated by Per Bak’s collapsing sand pile example.
See R. Kroszner, “Strategic Risk Management in an Interconnected World.” RMA Speech,
October 20, 2008
33
34
The question of how risk management adds value was discussed by C. Smithson and B.
Simkins, “Does Risk Management Add Value: A Survey of the Evidence”, Journal of Applied
Corporate Finance, 2005.
30
35
Research shows that firms that grew faster than 25 percent between 2004 and 2006
experienced trading and credit losses twice the level incurred at more stable firms during the
period. See A. Kucitzkes, Risk Governance: Seeing the Forest for the Trees, Oliver Wyman,
(October 14, 2008).
36
The October 23, 2008, congressional testimony of former Federal Reserve Chairman A.
Greenspan highlights this probability neglect. He states that two decades of data caused him
to commit a policy error concerning the ability of institutions to act in their self-interest.
37
The risk compensation concept was developed by J. Adams. He noticed that seat-belt laws
did not reduce fatalities. Rather, drivers tended to drive faster. Pedestrian and cyclists deaths
increased thereby offsetting the seatbelt benefits to drivers.
38
Mispricing hidden catastrophic event risk in structured products was illustrated in J. Coval, J.
Jurek, and E. Stafford, “Economic Catastrophic Bonds”, The American Economic Review, Vol.
99, No. 3 (June, 2009) 628. This showed that taking equivalent alternative exposures in the
underlying assets yielded a significantly higher return. The mispricing is attributed to the
increased demand for the less transparent structured securities, which can be used to exploit
risk management systems.
39
Structured finance can be viewed as a compensation scheme masquerading as a business.
40
AAA-rated structured products received premium spreads over the nonstructured corporate
AAA instruments which further enhanced their demand by naive investors. This raises
questions over the accuracy of the rating.
41
The shadow banking system of unregulated credit providers such as hedge funds greatly
expanded endogenous liquidity. This led to a false sense of security concerning the
continuing availability of such liquidity. The subsequent demise of this system triggered a
painful liquidity squeeze.
42
As R. Bookstabler noted in his June 19, 2008 Senate Testimony, in a crisis the key issues
are who owns what, the pressure they are under to liquidate and what else they own.
See R. Stulz, “Risk Management Failures: What Are They and When Do They Happen?”
Journal of Applied Corporate Finance, vol. 20 no. 4 (Fall 2008).
43
44
Information asymmetry is a condition where relevant information is not equally shared
among participants. It underlies agency problems where management, the agent, can exploit
shareholders, principals, because they know more.
45
See the 2002 annual report of Berkshire Hathaway.
See B. Nocco and R. Stulz. “Enterprise Risk Management: Theory and Practice”, Journal of
Applied Corporate Finance, 18:4, fall, 2008, for a good overview.
46
Adopted from Booz Allen and Hamilton, “Redefining the Corporate Governance Agenda,”
June 2003.
47
31
48
Similar to the concepts outlined by N. Taleb, Antifragile, (Random House, NY) 2012.
See “Staff Report”, U.S. House of Representatives Financial Services Subcommittee on
Oversight and Investigations Committee on Financial Services (November 15, 2012).
49
See “JP Morgan Chase Whale Trades: A Case History of Derivatives, Risks and Abuses”,
U.S. Senate Committee on Banking, Housing and Urban Affairs (March 15, 2013).
50
See R. Stulz, “Rethinking Risk Management”, Journal of Applied Corporate Finance, Vol. 9,
No. 3, fall, 1996, for the First Rethink Over 15 Years Ago.
51
32