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The financial crisis has impacted enormously on two features that are critical for investors' decisions: their beliefs and their preferences. It has brought to light diffuse opportunistic behaviour and some serious frauds. Because of this, trust in banks, bankers and brokers and the stock market has collapsed to unprecedented levels. The fear following the crisis, and the symptoms of panic that went with it, have led investors to become much more risk averse than they used to be in the past. We argue that failing trust and risk tolerance have a major effect on the working of financial markets and the economy. We show evidence that suggests that the drop in trust and the increase in risk aversion are likely to be enduring and very slow to recover. This is one reason, perhaps among others, why the consequences of the financial crisis will probably be lasting.
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Risk aversion and nancial crisis
luigi guiso
On the traditional view, an explanation of economic phenomena that reaches
adierence in tastes between people or times is the terminus of the argument:
the problem is abandoned at this point to whoever studies and explains tastes
(psychologists? anthropologists? phrenologists? sociobiologists?). On our
preferred interpretation, one never reaches this impasse: the economist con-
tinues to search for dierences in prices or incomes to explain any dierences
or changes in behavior.
George Stigler and Gary Becker (1977)
Risk preferences are a key parameter for nancial decisions. They govern
portfolio choice and the demand for insurance, and they are central for
mortgage contract choice. More generally, they enter any decision that
has an element of risk in it. Economists have long tended to regard risk
preferences as a given attribute, possibly invariant over time and age and
possibly independent of circumstances. The typical and most diuse
characterization of preferences for risk the CRRA utility conforms
to this view. Under CRRA, risk tolerance is a constant parameter, inde-
pendent of age, independent of wealth and of the state of the world, but
possibly varying across individuals for reasons that economists have
often avoided exploring, partly because, in the classical division of
labor across disciplines, economistshavechosentoleavetheexplana-
tion of the origin of preferences and technologies to other interested
disciplines and focalize instead on variation in prices and endowments
as driving forces of behavior. This traditional view became rooted in
AXA Professor of Household Finance at the Einaudi Institute for Economics and Finance
(EIEF) and Fellow at the Centre for Economic Policy Research (CEPR).
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Economics after Stigler and Becker (1977) forcefully theorized it by
arguing that The establishment of the proposition that one may use-
fully treat tastes as stable over time and similar among people is the
central task of this essay.
Times have changed, and views too. It is now accepted that econo-
mists not only rely on tastes to understand behavior, they also try to
understand what drives dierences in preferences across individuals
and their changes over time, possibly linking these changes to eco-
nomic phenomena; preferences, far from being part of the data for an
economist, become part of the factors used to explain economic phe-
nomena. In turn, changes in the economic environment can alter
This link is most clear in asset pricing, where the idea that risk
preferences are invariant has long been abandoned. Models that assume
invariant preferences are in fact unable to account for the observed
variation in the prices of risky assets relying only on variation in assets
cash ows. Variation in the risk tolerance of individuals is required in
order to match the high variability that we observe in assets prices.
But do risk attitudes of individuals actually change over time? If so,
what drives variation in individualsrisk preferences? Are they driven by
economic factors or by psychological forces? How do preferences for risk
evolve dynamically? How enduring are variations in risk attitudes over
time? How should time-varying risk preferences be characterized? In this
chapter I will tackle these questions. I will discuss these issues, summar-
izing what we know about individual preferences for risk and motives for
them to change over time. I will also provide some evidence on how
much and why these preferences changed during the nancial crises. This
discussion provides some food for thought for a pending but important
issue: is there room for policy and regulatory interventions to aect
variation in risk preferences, and are interventions of this sort are desir-
able? Needless to say, part of the answer will depend on what drives
variation in risk preferences and on the eects of these variations on
policy relevant outcomes.
The rest of the chapter is organized as follows. In Why can willingness
to bear risk vary over time?I review several factors than can lead to
changing risk aversion, distinguishing between economic and non-
economic drivers. In Does willingness to bear risk actually vary over
time?I provide evidence of what actually matters for changing risk
aversion and show evidence of risk aversion changing during the last
nancial crisis. Conclusions follow.
risk aversion and financial crisis 291
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Why can willingness to bear risk vary over time?
The risk aversion that matters for assets pricing is the risk aversion of the
average investor. This can change over time because the distribution of
wealth across individuals with dierent but constant risk aversion
changes or because the risk aversion of the single individual changes.
Here, we will focus on changes in the risk aversion of the single investors.
In turn, there are two reasons why the willingness of the individual to
bear risk changes over time: because the risk aversion parameter of the
period utility function evolves, or because the individual endowment
evolves and risk preferences are sensitive to the movements of the
endowment, which could be the mean or its variance or even higher
Evolving risk aversion parameter
Suppose the utility function is CRRA, so that the period utility is
uðcÞ¼ c1λ
1λ; the individual relative risk aversion is λ. Rather than being
a constant, individualswillingness to bear risk can be made a function of
observables zit and λ=λðzitÞ. The set of observables can vary across
individuals and over time. Dierences across individuals contribute to
creating heterogeneity in risk aversion in a population, and potentially in
the aggregate risk aversion, as the distribution of wealth changes. Some of
the time variations in zit can be specic to the individual; some can be
common to all and thus shift the risk aversion of a whole population in
the same direction. The rst will normally have no eect on the aggregate
risk aversion except when idiosyncratic variations happen to be corre-
lated with the wealth of the individuals (and thus with the weights used to
aggregate the individual risk aversions); the second can move the overall
risk aversion and can have important eects on assets prices. As we will
see, nancial crises are episodes of the latter type. The literature has
identied several factors of both types.
Time-invariant characteristics
Before discussing them, it is worth noting that several time-invariant,
demographic characteristics have been found to correlate with individual
risk preferences. Thus, variation over time in the composition of the
population across groups with dierent risk aversion can result in varia-
tion over time in the average risk aversion of the population. For
instance, several papers nd that risk aversion is higher for women
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than for men.
Another robust cross-sectional nding is that education
has a positive impact on risk taking (e.g. Vissing-Jørgensen 2002). Recent
research has also established strong correlations between measures of
risk preferences and individual intelligence. Frederick (2006) nds that,
in a sample of students, laboratory measures of risk aversion are nega-
tively correlated with IQ scores. This result extends outside the lab and to
non-student samples (Dohmen et al. 2010, Beauchamp, Cesarini and
Johannesson 2011 [in a sample of Swedish twins], Grinblatt, Keloharju
and Linnainmaa 2011, Anderson et al., 2011). Since IQ seems to have a
time trend, this can generate a temporal pattern in the average risk
tolerance of the population. But because IQ does not evolve over the
business cycle, this channel cannot explain changes in risk aversion at the
business cycle frequency.
Interestingly, Anderson et al. (2011) also nd that specic components
of personality measures, in particular neuroticism (individualstendency
to experience negative emotional states such as anger, guilt and anxiety),
are also correlated with risk aversion. This is interesting because emo-
tional states, such as anger and guilt, are bound to change possibly at high
frequency. Anger, in particular, is a sentiment that, as documented by
Guiso, Sapienza and Zingales (2013b), is associated with nancial crisis
and can thus be a cause of increased risk aversion following episodes of
nancial collapse.
A recent and growing literature aims at assessing the geneticcomponent
of nancial risk taking by using data on the behavior of twins. Cesarini et al.
(2009) estimate that about 30 percent of the individual variation in risk
aversion elicited in experiments using hypothetical lotteries is due to
genetic variation. They also nd that the shared environmental component
(due, for example, to upbringing) is very small, and in some specications
close to zero.
Even though there is clear consensus on the existence of a genetic
component of risk taking, its magnitude is still under debate. A promis-
ing approach is taken by Dreber et al. (2009) and Kuhnen and Chiao
(2009) who look directly at the eect of actual genes on risk-taking
In experimental settings, e.g. Holt and Laury (2002) and Powell and Ansic (1997). Using
eld data and surveys, see Hartog, Ferrer-i-Carbonell and Jonker (2002), Dohmen et al.
(2011), Guiso and Paiella (2009), Kimball, Sahm and Shapiro (2007), among others.
Croson and Gneezy (2009) survey the literature and warn about the bias that only papers
nding a gender eect might end up being published.
Consistent with these features, Calvet and Sodini (2014) document that twins with
depression symptoms tend to have a lower share of nancial wealth invested in risky assets.
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behavior. They are able to nd a positive and signicant correlation
between risk taking and the lack or presence of specic alleles.
Finally, an emerging literature studies the role of specic biological
factors in shaping investors preferences. Particular attention has been
given to the eect of testosterone on risk attitudes. A growing number of
contributions study the eect of fetus exposure to testosterone during
pregnancy, as measured by the 2D:4D ratio, nding, so far, weak eects
(Garbarino et al. 2011, Sapienza, Zingales and Maestripieri 2009, Apicella
et al. 2008 and Guiso and Rustichini 2011 nd none).
Needless to say, while genetic factors and early experiences reecting
dierences in family backgrounds help explain persistent cross-sectional
dierences in risk attitudes, they cannot explain time variation in risk
attitudes among adults.
One demographic characteristic that can result in variation in risk atti-
tudes over time is age. Elicited risk aversion parameters tend to be
positively correlated with age (e.g. Dohmen et al. 2011, Barsky et al.
1998, Guiso and Paiella 2008); age may contribute to explaining patterns
of portfolio choice over the life-cycle, and even trends in risk aversion if
the age-distribution of the population changes, but per se cannot explain
variation in risk aversion over business cycles and thus the variation in
assets prices at the business cycle frequency.
Mood and fear
Emotions can cause changes in peoples willingness to bear risk.
Loewenstein (2000) argues that decisions are not made only on the basis
of anticipated results, as in a standard expected utility framework.
Emotions experienced at the time of decision-making (immediate emo-
tions) can also play a role, sometimes a key one. Emotions such as fear
originate in the brains limbic system (amygdala, cingulate gyrus and
hippocampus) and they are processed and moderated by the frontal cortex
(Pinel 2009). For instance, mood may be aected by weather conditions or
by exposure to light: people exposed to more light tend to be less risk
averse. Because light varies seasonally, this introduces a time variation in
risk aversion and in peoplesnancial decisions (Kamstra et al. 2003,
Kramer and Weber 2012).
A simple way to embed the role of emotions in the standard utility
framework is to assume that emotions can alter some parameter of an
individual utility function. That is, fear or some other risk aversion
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relevant emotions can be thought as a state-contingent increase in the
curvature of the utility function.
Insofar as a catastrophic event, either economic or non-economic,
triggers an emotional reaction such as fear, it can result in an increase in
risk aversion. This may explain why during downturns, and particularly
during nancial crises, investors who do not lose money directly also
become more risk averse, even with respect to known probabilities gam-
bles, as we will show in the second section. The terrifying news appearing
on television, interactions with friends who lost money in the market,
and the pictures of red people leaving their failed banks might have
triggered an emotional response. Of course, because during nancial crises
the value of the endowments changes also the hypothesis cannot be tested
with our data because it is observationally equivalent to a background risk
model. Does the picture of Lehmansred employees trigger an emotional
fear response, or does it increase the subjective probability of a very bad
A large literature in medicine and psychiatry, such as Holman and Silver
(1998), documents that exposure to traumas can produce complex and
long-lasting consequences on mental and physical health. Shaw (2000)
argues that major structural central nervous system changes occur from
birth to early adolescence. Traumatic experiences during these critical
stages may have a determining eect on brain structural development
and sympathetic nervous system responsivity, and the hypothalamic
pituitary adrenal axis
(see Lipschitz et al. 1998). Therefore, traumas
experienced early in life could reasonably aect adultsrisk-taking beha-
vior. Indeed, several papers from psychology and neuroscience suggest
that risk aversion has a specic neural basis and an important emotional
component (e.g. Kuhnen and Knutson 2005).
One strand of literature has focused on non-economic traumas in
particular, exposure to natural disasters as causes of change in peoples
risk attitudes. For example, Cameron and Shah (2012) nd that indivi-
duals, who recently experienced a ood or an earthquake in Indonesia
during the previous three years exhibit higher levels of risk aversion than
The sympathetic nervous system (one of three major parts of the autonomic nervous
system) is responsible for mobilizing the bodys nervous system ght-or-ight response.
The ght-or-ight response is a physiological reaction that occurs in response to a
perceived harmful event, attack or threat to survival.
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similar individuals living in villages in the same area who were not
touched by the disasters. Others nd that, as an immediate reaction to
a natural disaster, individuals tend to become less risk averse (Eckel et al.
2009, Page et al. 2012). There are still no studies of the long-term
consequences of traumatic natural disasters, such as an early-age experi-
ence of an earthquake.
Traumas can also be induced by large and unusual shocks, such as the
loss of a job or exposure to a nancial crisis. One small but inuential body
of research on the impact of life experiences on risk attitudes has investi-
gated the impact of macroeconomic events, such as nancial busts or the
great depression, on risk-taking behavior and peoples beliefs. Malmendier
and Nagel (2011) nd that birth-cohorts of people who have experienced
low stock market returns throughout their life report greater risk aversion,
are less likely to participate in the stock market and, if they participate,
invest a lower fraction of liquid wealth in stocks. Their estimates indicate
that experiencing macroeconomic events early in life aects risk-taking
behaviors, but recent realizations have a stronger impact than distant
ones. Fagereng, Gottlieb and Guiso (2013) nd similar results in a large
panel of Norwegian households: investors who, in impressionable
years(age 1823), were exposed to more macroeconomic uncertainty
invest a lower share in stocks over their lifetime.
These eects, though triggered by badeconomic events, are unlikely
to reect a relation between risk tolerance and wealth. In fact, wealth-
induced changes in risk preferences (such as those generated by habit
preferences, as we discuss below; see Evolving endowment and eco-
nomic environment) should revert quickly as wealth recovers over the
business cycle. Trauma-induced changes may instead be long-lasting.
Insofar as a nancial crisis is a traumatic experience for many, it can
induce large changes in risk aversion and, most importantly, this may be
long lasting, which may help explain why recoveries from nancial-
crisis-induced recessions are so slow.
Evolving endowment and economic environment
Risk preferences can change over time not because the concavity of
period utility changes in response to shocks, but because the individual
endowment and the economic environment change, and the structure of
preferences is such that peoples willingness to bear risk is sensitive to
variations in the distribution of the endowment or in the structure of the
economic environment. Changes of this sort fall in the tradition of
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economics: variations in willingness to bear risk are caused by changes in
economic endowments, and variation in the rst can, in turn, aect
equilibrium asset prices.
Financial wealth
One key variable is the level of nancial wealth. It is widely accepted, and
strongly supported by evidence, that the absolute risk aversion of an
individual decreases with the level of the endowment. More controversial
is the relation between the endowment and the relative risk aversion of an
individual. But it is the latter that matters for asset pricing. In order to
generate a link between relative risk aversion and the individual nancial
wealth one needs to depart from CRRA utility. Assume that relative risk
aversion depends on nancial wealth Wiaccording to λ=λðzitÞ
where γis an individual component that captures unobserved risk pre-
ferences and may depend as before on a vector zit of time-varying or
time-invariant characteristics. A value of η¼0. to constant relative risk
aversion, and we are back to the previous case in which relative risk
aversion can evolve over time because the risk aversion of period utility
changes. Positive values of ηimply decreasing relative risk aversion.
When nancial wealth increases peoples willingness to bear risk
increases, and vice versa. Hence, if η>0movements in personal wealth
over the business cycle, for instance caused by a drop or a boom in assets
prices, may result in swings in individual willingness to bear risk. Habit
persistence models such as those used by Constantinides (1990) and
Campbell and Cochrane (1999) have this property and this is the main
hypothesis that has been explored by economists. Needless to say, during
nancial downturns, and even more so during nancial crises, asset
values drop and the stock of wealth tends to get closer to the stock of
habits, causing risk aversion to increase. Hence, in principle, habit
models can explain time variation in risk aversion. One type of habit
that has been recently emphasized in the literature is consumption
commitments expenditures related to durable goods, such as housing
and cars that involve adjustment costs. Commitments can aect inves-
tor risk preferences (e.g. Grossman and Laroque 1990, Chetty and Szeidl
Put dierently, the deep preferences for risk do not vary; what changes is the risk aversion
of the indirect utility function.
risk aversion and financial crisis 297
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2007, Postlewaite, Samuelson and Silverman 2008). In particular, these
papers argue that commitments amplify risk aversion over moderate
shocks. Households with housing or expensive cars have an incentive
to reduce nancial risk exposure to make sure they can continue paying
their bills when hit by temporary shocks.
Despite the fact that habit preferences have been the main explanation
put forward by economists for time-varying risk aversion, this seems to
receive mixed empirical support when tested on micro data. For instance,
Brunnermeier and Nagel (2008) nd that one key implication of habit
models that the portfolio share invested in risky assets should correlate
positively with the level of wealth does not hold in a sample of US
households. Chiappori and Paiella (2011) run a similar test in a panel of
Italian households and cannot reject that the risky portfolio share is
unaected by variation in households wealth, leading them to conclude
that household preferences are well represented by CRRA utility, and
thus to reject the habit model as an explanation for variation over time in
preferences for risk.
Lupton (2002) and Calvet and Sodini (2014) nd instead evidence that
is more consistent with the habit model. They test directly habit forma-
tion models on household portfolio allocation decisions by using proxies
for habit measured with US and Swedish data. They note that habit
formation models carry four testable predictions. The portfolio risky
share should decrease with proxies for habit and increase with nancial
wealth. Additionally, the nancial wealth elasticity of the risky share
should not only be positive but also heterogeneous across investors. It
should decrease with nancial wealth and increase with the habit. Lupton
(2002) tests the eect of internal habit on the risky share in the cross
section, nding support for habit formation models. Calvet and Sodini
(2014) document the same result with Swedish data, and argue that habit
has a causal eect on the risky share by using twin regressions. They also
nd that the nancial wealth elasticity of the risky share is decreasing in
wealth and increasing in proxies for habit. Finally, Chetty and Szeidl
(2008) provide some empirical evidence that households with more
commitments follow more conservative nancial portfolio strategies.
One issue with this evidence is that, instead of capturing a relation
between habits and risk aversion, any correlation between the risky share
and wealth may reect some relation between wealth and other determi-
nants of the portfolio risky share, such as information which may evolve
with wealth. To isolate the risk aversion channel, one would require
direct measures of risk aversion and of their evolution over time.
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Guiso, Sapienza and Zingales (2013b) use a measure of this sort and nd
mixed evidence. We will return to their evidence below, in Does will-
ingness to bear risk actually vary over time?
Background risk and access to credit markets
Background risk is probably the most widely cited environmental factor
used to explain heterogeneity in risk attitudes. It can be dened as a type of
risk that cannot be avoided because it is non-tradable and non-insurable.
Under some regularity assumptions on preferences, background risk makes
investors less willing to take other forms of risks, such as investment in risky
nancial assets. Researchers have identied sources of background risk in
wealth components that cannot be fully diversied because of market
incompleteness or illiquidity. Human capital (e.g. Bodie, Merton and
Samuelson 1992, Viceira 2001, Cocco, Gomes and Maenhout 2005), hous-
ing wealth (e.g. Cocco 2005, Yao and Zhang 2005) and private business
wealth (Heaton and Lucas 2000) have been used to explain the reluctance of
households to invest in risky nancial markets. Dierently from habits
which are concerned with the rst moment of the distribution of the
endowment, background risk arises in relation to variation in the second
moment. The latter in turn may vary over the business cycle, and increase
during downturns (Pistaferri and Meghir 2004).
In addition to background risk, Gollier (2006) argues that risk prefer-
ences might also be aected by limited access to credit markets since it
restricts the ability of households to transfer risk in time. Borrowing
constraints make investors more risk averse in anticipation of the possi-
bility that the constraint might be binding in the future (Grossman and
Vila 1992). Finally, background risk might also be aected by household
size and composition, as the probability of divorce and the random
liquidity needs of a larger family with children might discourage nancial
risk taking (Love 2010). Needless to say, credit market accessibility tends
to be more severe during downturns, and even more so during nancial
crises, when intermediaries restrict credit-granting criteria and credit
crunches emerge. Hence, this channel too has a potential for inducing
increased risk aversion in downturns and in particular during nancial
Empirical evidence on background risk and risk-taking behavior rely
mostly on cross-sectional evidence. Guiso, Jappelli and Terlizzese (1996),
Guiso and Jappelli (1998), and Palia, Qi and Wu (2014) nd that inves-
tors with more uncertain labor income, facing tighter borrowing con-
straints, buy more insurance and tend to participate and invest less in
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equity markets. Guiso and Paiella (2008) document that households
living in areas with more volatile aggregate income growth are more
risk averse when oered a hypothetical lottery. Hung et al. (2014) nd
that in Taiwan, individuals employed at listed companies with greater
idiosyncratic return volatilities are less likely to invest in equity in gen-
eral, and in their employers stock in particular. Betermier et al. (2011)
nd that a household moving from an industry with low wage volatility to
one with high wage volatility will, ceteris paribus, decrease its portfolio
share of risky assets by up to 35 percent. Heaton and Lucas (2000a) nd
that entrepreneurial households with more private business wealth hold
less in stocks relative to other liquid assets. Similarly, they nd that
workers with stocks in the rm they work for have a lower portfolio
share of common stocks. Cocco (2005) and Yao and Zhang (2005)
calibrate life-cycle models of optimal portfolio decisions with data from
the PSID and document a background risk component of housing wealth
that crowds out equity holdings.
The cross-sectional literature cannot distinguish the direct eect of
background risk from the extent to which it proxies for latent character-
istics. Panel analysis, on the other hand, might be problematic since some
forms of background risk, such as human capital, are highly persistent
and others, such as housing wealth, might be endogenous to nancial
decisions. Calvet and Sodini (2014) use twin regressions to shed light on
this issue and conrm the importance of background risk on nancial
risk taking. They verify the cross-sectional ndings that self-employed
and credit-constrained twins with more volatile income invest less in
equity markets.
Persistence and contagion
How persistent can changes in risk aversion be over time? Answering this
question is important. If changes are (possibly small and) short lived, so
are their consequences. Furthermore, individuals may be aware that their
attitude is subject to temporary uctuations and thus act on the expected
value of their risk aversion. In this case, the traditional characterization of
risk preferences as a stable individual trait may be a reasonable assump-
tion to characterize behavior. If instead departures are (large and) per-
sistent, they may have enduring consequences. And even if individuals
understand these swings in their preferences for risk, they may nd it
dicult to ignore them.
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Persistence of changes in risk aversion is likely to dier depending on
the cause of the change and the size of the shock. Changes induced by
variation in mood, such as those due to light exposure (Kramer et al.
2012), variation in the blood levels of testosterone (Sapienza, Zingales
and Maestripieri 2009) or even fear-inducing (though not traumatic)
experiences are very likely to revert quickly as the cause of this change
reverts too. Variation induced by age is by denition permanent and
irreversible. The persistence of scary and traumatic experiences is more
problematic to assert. Some early-age traumatic experiences are likely to
have permanent consequences. The evidence in Malmendier and Nagel
(2011) that birth-cohorts of people who have experienced low stock
market returns throughout their life report greater risk aversion, is
consistent with long-lived eects of traumatic experiences. Some of
these experiences can persist even longer than the lifetime of the indivi-
dual who has experienced them, if, as shown by Dohmen et al. (2011),
risk aversion transmits across generations.
Finally, variation in risk aversion due to changes in the level of wealth
in habit models persists for as long as it takes for wealth to revert back to
normal. Large drops in wealth may be slow to rebuild, particularly after a
nancial crisis, implying that increases in risk aversion following a
nancial depression can be slow to recover. Hence, habit models can
explain relatively long-lasting changes in risk aversion but cannot explain
changes that last beyond the change in wealth. A similar consideration
applies to cyclical changes in background risk and householdsaccess to
the credit market.
To explain large uctuations in assets prices, variation in risk aversion
must be common to a substantial portion of the investors. This is the case
if risk aversion responds to aggregate shocks, such as a drop in wealth due
to a nancial crisis. Idiosyncratic variations due to, for instance, changes
in mood will tend to wash out. Yet, there is evidence that emotions can be
contagious, so an event experienced by a fraction of the population that
makes them cautious may spill over to others, thereby increasing their
cautiousness too. In an experiment on Facebook, Kramer et al. (2014)
show that emotional states can be transferred to others through emo-
tional contagion, which leads people to experience the same emotions
even without their awareness. Hence, a traumatic experience such as
fear that hits a relatively large portion of the population and raises their
level of risk aversion can have a similar eect on the remaining portion.
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The media and social networking (as in the Kramer et al. (2014) experi-
ment) can be the vehicle of contagion.
Does willingness to bear risk actually vary over time?
The observation that the price of risk varies over time is consistent with
uctuations in investorsrisk tolerance, but it is no proof of it. A more
direct approach is to rely on direct measures of risk aversion elicited in
starting to employ. There are two big advantages in using direct mea-
sures of individualsrisk aversion. The rst is that one can directly
document whether individualsrisk aversion has a time-varying com-
ponent and thus check directly whether it is the risk aversion of the
individuals that leads to a change in aggregate risk aversion or whether
it is the distribution of wealth that changes, altering the aggregate risk
aversion with no change in the risk aversion of the individual investors.
The second is that one can test dierent explanations of what produces
the changes and possibly distinguish among the various forces dis-
cussed in Section 2. The main shortcoming is that the collection of
data on elicited risk aversion has only started recently and there is little
panel data.
One useful source that has a relatively long time span is the Survey
of Consumer Finances. Since 1989 it has included a question meant to
elicit investorslevels of risk aversion. In the SCF each participant is
asked: Which of the following statements comes closest to the amount
of nancial risk that you are willing to take when you make your
nancial investment? : (1) Take substantial nancial risks to earn
substantial returns; (2) Take above-average nancial risks, expecting
to earn above-average returns; (3) Take average nancial risks, expect-
ing to earn average returns; (4) Not willing to take any nancial risks.
Answerstothisquestionallowtheclassication of investors according
to their level of risk aversion.
In a world where people face the same risk-return trade-os and make
portfolio decisions according to Mertons formula, their risk/return
choice reects their degree of relative risk aversion. In such a world, the
answers to the above question can fully characterize peoples relative risk
preferences. People opting for low-risk/low-return combinations are also
individuals with higher risk aversion. Table 13.1 shows the distribution of
the answers to these questions in all SCFs where it was asked, including
the last one.
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There are a number on intriguing features in this table. First, and most
importantly, there is substantial increase in risk aversion following the
nancial crisis. The fraction of risk-tolerant individuals dened as those
answering either (1) or (2) was 26.6 percent in 2007, before the nancial
crisis, and drops to 16.9 percent in 2010 after the crisis (last row);
similarly, the percentage of individuals that prefer no nancial risk,
even if this entails very low returns, jumps from 31.2 percent in 2007 to
47.4 percent in 2010, as is made clear in Figure 13.1.
This is consistent with risk aversion changing dramatically during the
most recent nancial crisis. The second feature is that risk aversion was
higher than average in 1989 and then dropped continuously in the
subsequent surveys. The share of people answering no riskwas around
40 percent in 1989 and fell to 30 percent over 11 years. The rst SCF
following the stock market crash of 1987 was in 1989. Based on the
patterns shown by the measure in 2007/2010 it is tempting to conclude
Table 13.1 Evolution of the distribution of risk preferences among US
1989 1992 1995 1998 2001 2004 2007 2010
1. Substantial risk
and return
4.91 5.08 5.15 6.09 5.8 5.12 5.17 3.51
2. Above-average
risk and return
12.24 16.09 18.64 23.34 23.17 20.25 21.42 13.38
3. Average risk
and return
42.27 39.69 41.88 40.26 40.1 41.5 42.2 36.76
4. No nancial
40.58 39.14 34.33 30.31 30.93 33.13 31.2 47.35
Risk tolerant
(1 or 2)
17.15 21.17 23.79 29.43 23.75 25.37 26.59 16.89
The table shows the distribution of a qualitative measure of risk aversion in the
survey of consumer nances. Investors are asked their preferences about risk and
returns when making their portfolio choices. They face four alternatives: 1) Take
substantial nancial risks to earn substantial returns; 2) Take above-average
nancial risks, expecting to earn above-average returns; 3) Take average nancial
risks, expecting to earn average returns; 4) Not willing to take any nancial risks.
The table shows the frequency distribution of the answers to this question. The last
row shows the percentage of people answering either 1 or 2.
risk aversion and financial crisis 303
[290–312] 26.5.2015 3:47PM
that the high level of risk aversion in 1989 reects an increase due to the
nancial collapse of 1989. Unfortunately, we cannot prove this; but if this
interpretation were true, then it would also show that an increase in risk
aversion after a scary episode such as a major nancial crisis takes
considerable time to revert. Indeed, the fact that investors still show a
great reluctance to assume nancial risk in 2010 compared to 2007 that
is, two years after the collapse of Lehman Brothers and even after the
stock market recovered suggests that increases in risk aversion of this
sort tend to be long-lasting.
The SCF data refer to a sequence of cross-sections, not to panel data.
Thus, they are informative of the evolution of the risk aversion of the
average investor but not of the risk aversion of the single investor. In
addition to this there are two other problems with the SFC measure.
First, because of their cross-sectional nature, they cannot easily be
used to test dierent factors that can explain the change in risk
tolerance. For instance, with this data it is hard to test whether risk
aversion has increased more (or mostly) for those who incurred
30 35 40 45
No financial risk
1995 2000
2005 2010
Figure 13.1 Share of highly risk-averse people in the Survey of Consumer Finances
[Subtext gure: The gure shows the proportion of people answering Not willing to
take any nancial riskto the risk aversion question asked in the Survey of Consumer
Finances described in Table 13.1, year by year.]
304 investor and borrower protection
[290–312] 26.5.2015 3:47PM
nancial losses during the crisis, as would be predicted by habit
models. One could bypass this problem by constructing averages of
risk aversion and endowments (and other explanatory variables) for
over time (and age) that is, setting up a pseudo-panel. Clearly, the
results would be conditional on the grouping criteria. Second, if people
dier in beliefs about stock market returns and/or volatility, these
dierences will tend to contaminate the answers to the SCF question.
This bias would aect not only cross-sectional comparisons, but also
inter-temporal ones, possibly revealing a change in risk preferences
when none is present.
In a recent paper, Guiso, Sapienza and Zingales (2013b) try to over-
come these problems. First, they elicit a measure equivalent to the SCF
one but in a sample of Italian investors interviewed before the nancial
crisis (in 2007), and then after the collapse of Lehman Brothers, in the
spring of 2009. For this panel of investors they have several measures of
their assets as well as various characteristics and information on their
expectations about stock market returns and volatility, allowing them to
assess whether the latter played a role in aecting risk attitudes. Being a
panel, they can look at correlations between changes in risk aversion and
changes in potential determinants.
Second, they obtain an additional measure of risk aversion that is not
contaminated by changing beliefs. Each respondent was presented with
several choices between a risky prospect, which paid EUR 10,000 or
EUR 0 with equal probability and a sequence of certain sums of money.
These sums were increased progressively between EUR 100 and EUR
9,000. More risk-averse people will give up the risky prospect for lower
certain sums. Thus, the rst certain sum at which an investor switches
from the risky to the certain prospect identies (an upper bound for)
his/her certainty equivalent, from which they obtain the investor risk
Using these measures, they document a remarkable shift in risk pre-
ferences. As in the SCF, the fraction of individuals who answer that
they normally are not willing to take any nancial risk increases from
18 percent in 2007 to 42 percent in 2009. Similarly, the risk premium the
median investor in willing to pay to avoid the secure safe lottery prospect
increases from EUR 1,000 in 2007 to EUR 3,500 in 2009. This corre-
sponds to a doubling of the tripling of the median investor risk aversion.
They show that the change in the distribution of wealth plays essentially
no role in explaining the change in the investors aggregate risk aversion,
risk aversion and financial crisis 305
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which is entirely due to the changes in the risk aversion of the individual
Guiso, Sapienza and Zingales (2013b) try to test various channels
that could potentially explain these patterns. Though changes in these
measures of risk aversion predict participation rates in the stock mar-
ket, they do not correlate with changes in investor wealth except for
those who experienced very large losses during the nancial crisis. But
risk aversion increases substantially even among investors who suered
very mild losses and, most importantly, among those who suered no
losses at all because they held no stocks in the summer of 2008 when the
crisis begun. The latter experienced an increase in risk aversion as large
as the former. This evidence is hard to reconcile with pure habit models,
though it may be consistent with changes in expected future incomes
and background risk. However, Guiso et al. (2013) check whether risk
aversion increased more among investors that are less likely to face
background risk (such as public employees or the elderly) and nd no
evidence in support of this either. What, then, has driven the change?
They advance a conjecture: fear. People reacted to the crisis by becom-
ing more fearful, and this fear automatically triggered higher risk
aversion. This explanation follows evidence in neuro-economics and
lab experiments that risk aversion is augmented by panic and fear.
Kuhnen and Knutson (2005) nd that more activation in the anterior
insula (the brain area where anticipatory negative emotions are pre-
sumably located) is followed by increased risk aversion. Kuhnen and
Knutson (2011) nd that subjects exposed to visual cues that induced
anxiety were subsequently more risk averse and less willing to invest in
risky assets. In support of this view, they nd that the increase in risk
aversion is correlated with measures of Knightian uncertainty. In addi-
tion, to nd some indirect conrming evidence, they ran an experiment
with a sample of students at Northwestern University, treating half of
the sample with a scary movie and then eliciting risk aversion from all
participants using the same questions that they asked the sample of
investors. They found that people who had watched the movie were
systematically more risk averse than those who had not been exposed to
the movie. Most importantly, the dierence in risk aversion between
the two groups was sizeable as sizeable as was the increase in risk
aversion during the nancial crisis. While this is no direct proof that the
increase in risk aversion during the nancial crisis was triggered by fear,
it shows that a fear mechanism has the potential to explain large swings
306 investor and borrower protection
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in risk aversion such as those documented in the SCF and in the Italian
It is well documented that recovery from nancial crises tends to be
slow, much slower than recovery from standard recessions. They may
also have more persistent eects,evenonthelevelofpotentialoutput
and long-term growth an issue that is receiving considerable attention
in the US (Hall, 2014) and which should be even more relevant in
Europe given the extremely slow recovery of the euro area as a whole,
particularly among the Southern European economies. The mechan-
isms generating the slow recovery and the persistent growth eects can
be several and they are not yet well understood. In this chapter we have
added another channel: increased investorsrisk aversion caused by the
crises. Increased risk aversion can aect the economy growth perfor-
mance directly by diverting entrepreneursinvestments from high-
growth but risky projects to safer but lower-growth investments; by
raising investorsrequired risk premium, and thus the cost of capital,
higher risk aversion can slow down recoveries because it lowers capital
accumulation. In addition, because it increases the relative cost of risky
capital, it can slow down growth because the relative cost of equity
investment increases, discouraging investment in innovative rms
which rely disproportionately on equity nance.
We have discussed several mechanisms through which peoples risk
tolerance can change over time. Some are due to variation in economic
variables, in particular the distribution of individual endowments or the
access to insurance and credit markets; others reect psychological forces
that trigger fear. The evidence on what leads to changing risk aversion is
just starting to accumulate. The available data suggests that both factors
economic and psychological seem to matter in explaining why risk
aversion increases in response to nancial crisis.
Is there room for policy and regulatory interventions to stabilize peoples
risk preferences and, if so, of what sort?Can policy makers intervene in the
psychological mechanisms that drive risk aversion during a nancial crisis?
Can governments, for instance, regulate the dissemination of information
or its tone through the high-speed channels of todaysworld,inorderto
pre-empt the contagion of fear and the propagation of a crisis? We have no
answer to these questions, but they are on the table.
risk aversion and financial crisis 307
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... A financial crisis generally leads to a decline in trust in financial institutions (Ahunov and Van Hove, 2020;Guiso, 2010Guiso, , 2012Knell and Stix, 2015;Sapienza and Zingales, 2012). Using WVS data covering 52 countries during the period 2010, Fungácová et al. (2019 report in a recent study that the only country level factor that significantly relates to trust in banks is the occurrence of a financial crisis. ...
... 1 Several studies report a decline of trust in banks after the outbreak of the financial crisis (Guiso, 2010(Guiso, , 2012Knell & Stix, 2015;Sapienza & Zingales, 2012). 2 Järvinen (2014) reports substantial variation in trust in banks across European countries. Consumer trust towards banking is the highest in Malta, while it is also high in Finland, Luxembourg, Estonia, and Germany. ...
Trust in financial institutions is widely considered important. However, a clear overview of studies on the drivers of trust is missing. We intend to fill this gap in the literature. After discussing why trust in financial institutions is important, we turn to its measurement, where we distinguish between trust in one's own institution and trust in institutions in general (narrow-scope and broad-scope trust), and discuss how these measures differ from generalized trust (i.e. trust in other people with whom there is no direct relationship). Finally, we survey the determinants of trust in financial institutions and discuss a wide range of drivers. First, trust in financial institutions depends on the economic situation: it behaves procyclically and is negatively affected by financial crises. Second, the behavior of financial institutions matters: prudent conduct, the provision of good services and financial health have a positive effect on trust. Third, although consumer characteristics also relate to trust, many of these relationships are context-dependent. Fourth, there is a positive association between narrow-scope trust on the one hand and broad-scope trust and generalized trust on the other. Last, policy measures and supervisory actions can help prevent loss of trust.
... . Risk aversion has a notable effect on the country's economy and entrepreneurial investments (Guiso, 2012). Therefore, it is worthwhile for researchers to examine many factors that could influence risk preference at the individual, firm and country level. ...
... Studies have shown that there is a correlation between pessimism and risk tolerance, as there is a belief among scholars that optimistic people are less riskaverse than pessimistic people (2008). In finance, Guiso (2012) found that investors who are optimistic and have a high level of trust are more likely to accept risky investments such as stocks. In other words, optimism decreases risk aversion. ...
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With the opening of the Stock Connect programs, the mainland China and Hong Kong stock markets are becoming more closely linked. In this paper, we develop a China’s stock market risk early warning system. The proposed early warning system consists of three components. First, we use value at risk (VaR) to identify the stock market risk in which stock market risk is divided into multiple categories instead of two categories. Second, we construct a comprehensive indicator system in which basic indicators, technical indicators, overseas return rate indicators, and macroeconomic indicators are considered simultaneously. Third, we use four machine learning models, namely long short-term memory (LSTM), gate recurrent unit (GRU), multilayer perceptron (MLP), and EXtreme Gradient Boosting algorithm (XGBoost), to predict China’s stock market risk. Experimental results show that: (1) Considering the macroeconomic indicators and basic indicators of Shanghai Composite Index (SSEC), ShenZhen Component Index (SZCZ) and Hang Seng Index (HSI) can significantly improve the performance of predicting China’s stock market risk. (2) The opening of SH-HK Stock Connect program improves the predictive performance, but the opening of SZ-HK Stock Connect program decreases the predictive performance. (3) The indicators related to Hong Kong become more important after the SZ-HK Stock Connect program.
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Trust in banks is key, especially in turbulent times. Using unique daily data for a representative panel of Dutch consumers, we examine to what extent the COVID-crisis has affected trust in banks' payment services. We have the following main findings. First, COVID-19 measures have affected trust in banks' payment services. The first lockdown increased narrow-scope trust (trust in consumers' own bank payment services) and broad-scope trust (trust in banks' payment services in general). The second lockdown decreased both notions of trust. The crisis measures impacted the trust of the elderly the strongest. Second, personal characteristics are significantly related to trust in banks' payment services. For example, we find that both types of trust are increasing with digital literacy and the ease of getting by with income. Third, narrow-scope trust is higher than broad-scope trust. The gap between trust in the own bank and trust in banks in general is highest for customers of small banks.
... shocks, e.g., to health (Decker and Schmitz, 2016), or by aggregate economic shocks such as the Great Recession (Guiso, 2012;Dohmen et al., 2015). Malmendier and Nagel (2011) suggest that pronounced aggregate economic shocks that individuals experience during childhood, such as the Great Depression, affect attitudes of entire cohorts throughout their lives. ...
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Economists increasingly recognise the importance of personality traits for socio-economic outcomes, but little is known about the stability of these traits over the life cycle. Existing empirical contributions typically focus on age patterns and disregard cohort and period influences. This paper contributes novel evidence for the separability of age, period, and cohort effects for a broad range of personality traits based on systematic specification tests for disentangling age, period and cohort influences. Our estimates document that for different cohorts, the evolution of personality traits across the life cycle follows a stable, though non-constant, age profile, while there are sizeable differences across time periods.
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The coronavirus disease (COVID-19) represents a large increase in background risk for individuals. Like the COVID-19 pandemic, extreme events (e.g. financial downturns, natural disasters, and war) have been shown to change attitudes towards risk. Using a risk apportionment approach, we examine whether risk aversion as well as higher order risk attitudes (HORAs) (prudence and temperance) have changed during COVID-19. This methodology allows us to measure model-free HORAs. We include prudence and temperance as higher order measures, as these two have been largely understudied under extreme events but are determinants of decisions related to the health and financial domains. Once we account for socio-demographic characteristics, we find an overall increase in risk aversion during COVID-19. We also find similar results using a hypothetical survey question which measures willingness to take risks. We do not find changes in prudence and temperance using the risk apportionment methodology.
We investigate the financial crisis as a cut‐off point in EU financial systems’ evolution and assess its effects on cross‐country convergence patterns. Before the crisis, we observe a rise in the centrality of the financial sector, driven by an upward convergent pattern of bank credit. This inflated leverage ratios across all economic sectors, and a catch‐up phenomenon is retrieved in the leveraging of the real sector. The crisis caused a general deleveraging across all economic sectors, with a downward convergence pattern characterizing the financial sector, and triggered an upward convergent pattern in the level of insurance products in households’ portfolios.
Moralistic trust arises when people believe others share their moral values. I examine whether trust in the finance industry has a moral foundation by comparing values and attitudes of finance professionals with those of the general population in two data sets: a unique data set on values of CFAs in 2016 paired with the World Value Survey and the European Social Survey. I show that differences in “ethical” values of finance professionals and members of the population are generally smaller in countries where people trust financial institutions more. But as trust increases, these value differences become larger. I show that selection helps reconcile the differences in the cross-sectional and time-series results. In periods of high trust in the finance industry, e.g. the pre-crisis period, finance professionals in the sample are less educated. While many are asked what they think about finance professionals, my results suggest that asking finance professionals what they think can provide insights into how trust evolves with selection into the industry.
This paper explores the main forces impacting diversity and the role of women at senior management and board level in finance. In addition, it offers a synopsis of selected research examining the board composition, corporate social responsibility and external corporate governance. We focus mainly on empirical papers that employ quasi-natural experiments and textual analysis to confirm the interdisciplinary nature of diversity. Further, we identify priorities for future research that can advance our understanding on this research area, and the broader field of financial studies, encompassing the growing interest in the boundaries between the economic, the psychological and the social.
Using error-free data on life-cycle portfolio allocations of a large sample of Norwegian households, we document a double adjustment as households age: a rebalancing of the portfolio composition away from stocks as they approach retirement and stock market exit after retirement. When structurally estimating an extended life-cycle model, the parameter combination that best fits the data is one with a relatively large risk aversion, a small per-period participation cost, and a yearly probability of a large stock market loss in line with the frequency of stock market crashes in Norway.
We conduct a detailed examination of the psychometric and empirical properties of some commonly used survey-based measures of risk preferences in a population-based sample of 11,000 twins. Using a model that provides a general framework for making inferences about the component of measured risk attitudes that is not due to measurement error, we show the measurement-error adjustment leads to substantially larger estimates of the predictive power of risk attitudes, of the size of the gender gap, and of the magnitude of the sibling correlation. Risk attitudes are predictive of investment decisions, entrepreneurship, and health behaviors such as smoking and drinking, are robustly associated with cognitive ability and personality, and our estimates are often larger than those in the literature. One implication of our results is that the small amounts of variation that the risk measures have previously been reported to explain are in part artifacts of imperfect measurement.
Each year an alarmingly high number of children are exposed to severe psychology cal trauma, ranging from accidents and natural disasters to abuse and community-based violence. How does trauma affect the developing blain, and in what ways if PTSD in children different from that in adults?
The financial crisis and ensuing Great Recession left the US economy in an injured state. In 2013, output was 13% below its trend path from 1990 through 2007. Part of this shortfall— 2.2 percentage points out of the 13—was the result of lingering slackness in the labor market in the form of abnormal unemployment and substandard weekly hours of work. The single biggest contributor was a shortfall in business capital, which accounted for 3.9 percentage points. The second largest was a shortfall of 3.5 percentage points in total factor productivity. The fourth was a shortfall of 2.4 percentage points in labor- force participation. I discuss these four sources of the injury in detail, focusing on identifying state variables that may or may not return to earlier growth paths. The conclusion is optimistic about the capital stock and slackness in the labor market and pessimistic about reversing the declines in total- factor productivity and the part of the participation shortfall not associated with the weak labor market. © 2015 by the National Bureau of Economic Research. All rights reserved.
I show that investment in housing plays a crucial role in explaining the patterns of cross-sectional variation in the composition of wealth and the level of stockholdings observed in portfolio composition data. Due to investment in housing, younger and poorer investors have limited financial wealth to invest in stocks, which reduces the benefits of equity market participation. House price risk crowds out stockholdings, and this crowding out effect is larger for low financial net-worth. In the model as in the data leverage is positively correlated with stockholdings.
We construct a set of household-level background risk variables to capture the covariance structure of three nonfinancial assets and two financial assets. These risks are in general statistically significant and economically important for a household's stock market participation and stockholdings. A one-standard-deviation increase in background risks reduces the participation probability by 11% and the stockholdings-to-wealth ratio by 4%. The volatilities of labor income, housing value, and business income reduce a household's participation and stockholdings. A household with labor income highly correlated with stock (bond) returns is less (more) likely to invest in stock.