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Risk aversion

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Title: Risk aversion  
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Subject: Risk premium, Exponential utility, Economics, Expected utility hypothesis, Loss aversion
Collection: Actuarial Science, Behavioral Finance, Economics of Uncertainty, Financial Risk, Risk, Utility
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Risk aversion

Risk aversion (red) contrasted to risk neutrality (yellow) and risk loving (orange) in different settings. Left graph: A risk averse utility function is concave (from below), while a risk loving utility function is convex. Middle graph: In standard deviation-expected value space, risk averse indifference curves are upward sloped. Right graph: With fixed probabilities of two alternative states 1 and 2, risk averse indifference curves over pairs of state-contingent outcomes are convex.

In economics and finance, risk aversion is the behavior of humans (especially consumers and investors), when exposed to uncertainty, to attempt to reduce that uncertainty. It is the reluctance of a person to accept a bargain with an uncertain payoff rather than another bargain with a more certain, but possibly lower, expected payoff. For example, a risk-averse investor might choose to put his or her money into a bank account with a low but guaranteed interest rate, rather than into a stock that may have high expected returns, but also involves a chance of losing value.


  • Example 1
  • Utility of money 2
  • Measures of risk aversion under expected utility theory 3
    • Absolute risk aversion 3.1
    • Relative risk aversion 3.2
    • Implications of increasing/decreasing absolute and relative risk aversion 3.3
    • Portfolio theory 3.4
  • Limitations of expected utility treatment of risk aversion 4
  • Risk aversion in the brain 5
  • Public understanding and risk in social activities 6
  • See also 7
  • References 8
  • External links 9


CE - Certainty equivalent; E(U(W)) - Expected value of the utility (expected utility) of the uncertain payment; E(W) - Expected value of the uncertain payment; U(CE) - Utility of the certainty equivalent; U(E(W)) - Utility of the expected value of the uncertain payment; U(W0) - Utility of the minimal payment; U(W1) - Utility of the maximal payment; W0 - Minimal payment; W1 - Maximal payment; RP - Risk premium

A person is given the choice between two scenarios, one with a guaranteed payoff and one without. In the guaranteed scenario, the person receives $50. In the uncertain scenario, a coin is flipped to decide whether the person receives $100 or nothing. The expected payoff for both scenarios is $50, meaning that an individual who was insensitive to risk would not care whether they took the guaranteed payment or the gamble. However, individuals may have different risk attitudes[1][2]

A person is said to be:

  • risk-averse (or risk-avoiding) - if he or she would accept a certain payment (certainty equivalent) of less than $50 (for example, $40), rather than taking the gamble and possibly receiving nothing.
  • risk-neutral - if he or she is indifferent between the bet and a certain $50 payment.
  • risk-loving (or risk-seeking) - if he or she would accept the bet even when the guaranteed payment is more than $50 (for example, $60).

The average payoff of the gamble, known as its expected value, is $50. The dollar amount that the individual would accept instead of the bet is called the certainty equivalent, and the difference between the expected value and the certainty equivalent is called the risk premium. For risk-averse individuals, it becomes negative, for risk-neutral persons it is zero, and for risk-loving individuals their risk premium becomes positive.

Utility of money

In expected utility theory, an agent has a utility function u(x) where x represents the value that he might receive in money or goods (in the above example x could be 0 or 100).

Time does not come into this calculation, so inflation does not appear. (The utility function u(x) is defined only up to positive linear affine transformation - in other words a constant offset could be added to the value of u(x) for all x, and/or u(x) could be multiplied by a positive constant factor, without affecting the conclusions.) An agent possesses risk aversion if and only if the utility function is concave. For instance u(0) could be 0, u(100) might be 10, u(40) might be 5, and for comparison u(50) might be 6.

The expected utility of the above bet (with a 50% chance of receiving 100 and a 50% chance of receiving 0) is,


and if the person has the utility function with u(0)=0, u(40)=50, and u(100)=100 then the expected utility of the bet equals 50, which is the same as the known utility of the amount 40. Hence the certainty equivalent is 40.

The risk premium is ($50 minus $40)=$10, or in proportional terms


or 25% (where $50 is the expected value of the risky bet: (\tfrac {1}{2} 0 + \tfrac{1}{2} 100). This risk premium means that the person would be willing to sacrifice as much as $10 in expected value in order to achieve perfect certainty about how much money will be received. In other words, the person would be indifferent between the bet and a guarantee of $40, and would prefer anything over $40 to the bet.

In the case of a wealthier individual, the risk of losing $100 would be less significant, and for such small amounts his utility function would be likely to be almost linear, for instance if u(0) = 0 and u(100) = 10, then u(40) might be 4.0001 and u(50) might be 5.0001.

The utility function for perceived gains has two key properties: an upward slope, and concavity. (i) The upward slope implies that the person feels that more is better: a larger amount received yields greater utility, and for risky bets the person would prefer a bet which is first-order stochastically dominant over an alternative bet (that is, if the probability mass of the second bet is pushed to the right to form the first bet, then the first bet is preferred). (ii) The concavity of the utility function implies that the person is risk averse: a sure amount would always be preferred over a risky bet having the same expected value; moreover, for risky bets the person would prefer a bet which is a mean-preserving contraction of an alternative bet (that is, if some of the probability mass of the first bet is spread out without altering the mean to form the second bet, then the first bet is preferred).

Measures of risk aversion under expected utility theory

Absolute risk aversion

The higher the curvature of u(c), the higher the risk aversion. However, since expected utility functions are not uniquely defined (are defined only up to affine transformations), a measure that stays constant with respect to these transformations is needed. One such measure is the Arrow–Pratt measure of absolute risk-aversion (ARA), after the economists Kenneth Arrow and John W. Pratt,[3][4] also known as the coefficient of absolute risk aversion, defined as


The following expressions relate to this term:

  • Exponential utility of the form u(c)=1-e^{-\alpha c} is unique in exhibiting constant absolute risk aversion (CARA): A(c)=\alpha is constant with respect to c.
  • Hyperbolic absolute risk aversion (HARA) is the most general class of utility functions that are usually used in practice (specifically, CRRA (constant relative risk aversion, see below), CARA (constant absolute risk aversion), and quadratic utility all exhibit HARA and are often used because of their mathematical tractability). A utility function exhibits HARA if its absolute risk aversion is a hyperbolic function, namely
A(c) = -\frac{u''(c)}{u'(c)}=\frac{1}{ac+b}

The solution to this differential equation (omitting additive and multiplicative constant terms, which do not affect the behavior implied by the utility function) is:

u(c) = \frac{(c-c_s)^{1-R}}{1-R}

where R=1/a and c_s = -b/a . Note that when a = 0 , this is CARA, as A(c) = 1/b = const , and when b=0 , this is CRRA (see below), as c A(c) = 1/a = const . See [5]

  • Decreasing/increasing absolute risk aversion (DARA/IARA) is present if A(c) is decreasing/increasing. Using the above definition of ARA, the following inequality holds for DARA:
\frac{\partial A(c)}{\partial c} = -\frac{u'(c)u'''(c) - [u''(c)]^2} = \frac{1}{n} \frac{dE(r)}{d\mu_n}

Limitations of expected utility treatment of risk aversion

The notion of using expected utility theory to analyze risk aversion has come under criticism from behavioral economics. Matthew Rabin has showed that a risk-averse expected-utility maximizing individual who,

from any initial wealth level [...] turns down gambles where she loses $100 or gains $110, each with 50% probability [...] will turn down 50-50 bets of losing $1,000 or gaining any sum of money.[11]

Rabin criticizes this implication of expected utility theory on grounds of implausibility. One solution to the problem observed by Rabin is that proposed by prospect theory and cumulative prospect theory, where outcomes are considered relative to a reference point (usually the status quo), rather than to consider only the final wealth.

Risk aversion in the brain

Attitudes towards risk have attracted the interest of the field of neuroeconomics and behavioral economics. A 2009 study by Christopoulos et al. suggested that the activity of a specific brain area (right inferior frontal gyrus) correlates with risk aversion, with more risk averse participants (i.e. those having higher risk premia) also having higher responses to safer options.[12] This result coincides with other studies,[13][14] that show that neuromodulation of the same area results in participants making more or less risk averse choices, depending on whether the modulation increases or decreases the activity of the target area.

Public understanding and risk in social activities

In the real world, many government agencies, e.g. Health and Safety Executive, are fundamentally risk-averse in their mandate. This often means that they demand (with the power of legal enforcement) that risks be minimized, even at the cost of losing the utility of the risky activity. It is important to consider the opportunity cost when mitigating a risk; the cost of not taking the risky action. Writing laws focused on the risk without the balance of the utility may misrepresent society's goals. The public understanding of risk, which influences political decisions, is an area which has recently been recognised as deserving focus. David Spiegelhalter is the Winton Professor of the Public Understanding of Risk at Cambridge University; a role he describes as "outreach".[15]

A vaccine to protect children against the three common diseases measles, mumps and rubella was developed and recommended for all children in several countries including the UK. However, a controversy arose around fraudulent allegations that it caused autism. This alleged causal link was thoroughly disproved,[16] and the doctor who made the claims was expelled from the General Medical Council. Even years after the claims were disproved, some parents wanted to avert the risk of causing autism in their own children. They chose to spend significant amounts of their own money on alternatives from private doctors. These alternatives carried their own risks which were not balanced fairly; most often that the children were not properly immunized against the more common diseases of measles, mumps and rubella.

Mobile phones may carry some small[17][18] health risk. While most people would accept that unproven risk to gain the benefit of improved communication, others remain so risk averse that they do not. (The COSMOS cohort study continues to study the actual risks of mobile phones.)

Risk aversion theory can be applied to many aspects of life and its challenges, for example:

A recent experimental study with student-subject playing the game of the TV show Deal or No Deal finds that people are more risk averse in the limelight than in the anonymity of a typical behavioral laboratory. In the laboratory treatments, subjects made decisions in a standard, computerized laboratory setting as typically employed in behavioral experiments. In the limelight treatments, subjects made their choices in a simulated game show environment, which included a live audience, a game show host, and video cameras.[19] In line with this, studies on investor behavior find that investors trade more and more speculatively after switching from phone-based to online trading[20][21] and that investors tend to keep their core investments with traditional brokers and use a small fraction of their wealth to speculate online.[22]

See also


  1. ^ Virine, L., & Trumper, M. ProjectThink. Gower. 2013
  2. ^ Hillson D. & Murray-Webster R. Understanding and Managing Risk Attitude. Gower. 2007.
  3. ^ a b Arrow, K. J. (1965). "Aspects of the Theory of Risk Bearing". The Theory of Risk Aversion. Helsinki: Yrjo Jahnssonin Saatio.  Reprinted in: Essays in the Theory of Risk Bearing, Markham Publ. Co., Chicago, 1971, 90–109.
  4. ^ a b Pratt, J. W. (1964). "Risk Aversion in the Small and in the Large".  
  5. ^ "Zender's lecture notes". 
  6. ^ Levy, Haim (2006). Stochastic Dominance: Investment Decision Making under Uncertainty (Second ed.). New York: Springer.  
  7. ^ Friend, Irwin; Blume, Marshall (1975). "The Demand for Risky Assets".  
  8. ^ Bellemare, Marc F.; Brown, Zachary S. (2010). "On the (Mis)Use of Wealth as a Proxy for Risk Aversion".  
  9. ^ Simon, Carl and Lawrence Blume. Mathematics for Economists (Student ed.). Viva Norton. p. 363.  
  10. ^ Benchimol, J. (2014). "Risk aversion in the Eurozone".  
  11. ^ Rabin, Matthew (2000). "Risk Aversion and Expected-Utility Theory: A Calibration Theorem".  
  12. ^ Christopoulos GI; Tobler PN; Bossaerts P; Dolan RJ; Schultz W (2009). "Neural Correlates of Value, Risk, and Risk Aversion Contributing to Decision Making under Risk.". J Neurosci 26 (24): 6469–6472.  
  13. ^ Knoch D, Gianotti LR, Pascual-Leone A, Treyer V, Regard M, Hohmann M, Brugger P; Gianotti; Pascual-Leone; Treyer; Regard; Hohmann; Brugger (2006). "Disruption of right prefrontal cortex by low-frequency repetitive transcranial magnetic stimulation induces risk-taking behavior". J Neurosci 26 (24): 6469–6472.  
  14. ^ Fecteau S, Pascual-Leone A, Zald DH, Liguori P, Théoret H, Boggio PS, Fregni F; Pascual-Leone; Zald; Liguori; Théoret; Boggio; Fregni (2007). "Activation of prefrontal cortex by transcranial direct current stimulation reduces appetite for risk during ambiguous decision making". J Neurosci 27 (23): 6212–6218.  
  15. ^ Spiegelhalter, David (2009). "Don's Diary" (PDF). CAM - the Cambridge Alumni Magazine (The University of Cambridge Development Office) 58: 3. 
  16. ^ Madsen KM, Hviid A, Vestergaard M; et al. (2002). "A population-based study of measles, mumps, and rubella vaccination and autism". N Engl J Med 347 (19): 1477–82.  
  17. ^ "What are the health risks associated with mobile phones and their base stations?". Online Q&A.  
  18. ^ "Electromagnetic fields and public health: mobile telephones and their base stations". Fact sheet N°193. World Health Organization. June 2000. Retrieved 2008-01-19. 
  19. ^ Baltussen, Guido; van den Assem, Martijn; van Dolder, Dennie. "Risky Choice in the Limelight".  
  20. ^ Barber, Brad; Odean, Terrance (2001). "The Internet and the Investor".  
  21. ^ Barber, Brad; Odean, Terrance (2002). "Online Investors: Do the Slow Die First?".  
  22. ^ Konana, Prabhudev; Balasubramanian, Sridhar (2005). "The Social–Economic–Psychological model of technology adoption and usage: an application to online investing".  

External links

  • Closed form solution for a consumption savings problem with CARA utility
  • Paper about problems with risk aversion
  • Economist article on monkey experiments showing behaviours resembling risk aversion (requires a paid subscription to
  • Arrow-Pratt Measure on
  • Risk Aversion of Individuals vs Risk Aversion of the Whole Economy
  • The benefit of utilities: a plausible explanation for small risky parts in the portfolio
  • Risk Appetizer: Tool to determine the individual risk appetite for a one-off investment problem
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