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Stochastic dominance

For other uses, see Dominance.

Stochastic dominance is a partial order between random variables. It is a form of stochastic ordering. The concept arises in decision theory and decision analysis in situations where one gamble (a probability distribution over possible outcomes, also known as prospects) can be ranked as superior to another gamble for a broad class of decision-makers. It is based on shared preferences regarding sets of possible outcomes and their associated probabilities. Only limited knowledge of preferences is required for determining dominance. Risk aversion is a factor only in second order stochastic dominance.

Stochastic dominance does not give a total order, but rather only a partial order: for some pairs of gambles, neither one stochastically dominates the other, since different members of the broad class of decision-makers will differ regarding which gamble is preferable without them generally being considered to be equally attractive.

Contents

The simplest case of stochastic dominance is statewise dominance (also known as state-by-state dominance), defined as follows:

Random variable A is statewise dominant over random variable B if A gives at least as good a result in every state (every possible set of outcomes), and a strictly better result in at least one state.

For example, if a dollar is added to one or more prizes in a lottery, the new lottery statewise dominates the old one because it yields a better payout regardless of the specific numbers realized by the lottery. Similarly, if a risk insurance policy has a lower premium and a better coverage than another policy, then with or without damage, the outcome is better. Anyone who prefers more to less (in the standard terminology, anyone who has monotonically increasing preferences) will always prefer a statewise dominant gamble.

Statewise dominance is a special case of the canonical first-order stochastic dominance (FSD), which is defined as:

Random variable A has first-order stochastic dominance over random variable B if for any outcome x, A gives at least as high a probability of receiving at least x as does B, and for some x, A gives a higher probability of receiving at least x. In notation form, P [ A x ] P [ B x ] {\displaystyle P[A\geq x]\geq P[B\geq x]} for all x, and for some x, P [ A x ] > P [ B x ] {\displaystyle P[A\geq x]>P[B\geq x]} .

In terms of the cumulative distribution functions of the two random variables, A dominating B means that F A ( x ) F B ( x ) {\displaystyle F_{A}(x)\leq F_{B}(x)} for all x, with strict inequality at some x.

Gamble A first-order stochastically dominates gamble B if and only if every expected utility maximizer with an increasing utility function prefers gamble A over gamble B.

First-order stochastic dominance can also be expressed as follows: If and only if A first-order stochastically dominates B, there exists some gamble y {\displaystyle y} such that x B = d ( x A + y ) {\displaystyle x_{B}{\overset {d}{=}}(x_{A}+y)} where y 0 {\displaystyle y\leq 0} in all possible states (and strictly negative in at least one state); here = d {\displaystyle {\overset {d}{=}}} means "is equal in distribution to" (that is, "has the same distribution as"). Thus, we can go from the graphed density function of A to that of B by, roughly speaking, pushing some of the probability mass to the left.

For example, consider a single toss of a fair die with the six possible outcomes (states) summarized in this table along with the amount won in each state by each of three alternative gambles:

State (die result) 1 2 3 4 5 6 Gamble A wins $ 1 1 2 2 2 2 Gamble B wins $ 1 1 1 2 2 2 Gamble C wins $ 3 3 3 1 1 1 {\displaystyle {\begin{array}{rcccccc}{\text{State (die result)}}&1&2&3&4&5&6\\\hline {\text{Gamble A wins }}\$&1&1&2&2&2&2\\{\text{Gamble B wins }}\$&1&1&1&2&2&2\\{\text{Gamble C wins }}\$&3&3&3&1&1&1\\\hline \end{array}}}

Here gamble A statewise dominates gamble B because A gives at least as good a yield in all possible states (outcomes of the die roll) and gives a strictly better yield in one of them (state 3). Since A statewise dominates B, it also first-order dominates B. Gamble C does not statewise dominate B because B gives a better yield in states 4 through 6, but C first-order stochastically dominates B because Pr(B ≥ 1) = Pr(C ≥ 1) = 1, Pr(B ≥ 2) = Pr(C ≥ 2) = 3/6, and Pr(B ≥ 3) = 0 while Pr(C ≥ 3) = 3/6 > Pr(B ≥ 3). Gambles A and C cannot be ordered relative to each other on the basis of first-order stochastic dominance because Pr(A ≥ 2) = 4/6 > Pr(C ≥ 2) = 3/6 while on the other hand Pr(C ≥ 3) = 3/6 > Pr(A ≥ 3) = 0.

In general, although when one gamble first-order stochastically dominates a second gamble, the expected value of the payoff under the first will be greater than the expected value of the payoff under the second, the converse is not true: one cannot order lotteries with regard to stochastic dominance simply by comparing the means of their probability distributions. For instance, in the above example C has a higher mean (2) than does A (5/3), yet C does not first-order dominate A.

The other commonly used type of stochastic dominance is second-order stochastic dominance. Roughly speaking, for two gambles A {\displaystyle A} and B {\displaystyle B} , gamble A {\displaystyle A} has second-order stochastic dominance over gamble B {\displaystyle B} if the former is more predictable (i.e. involves less risk) and has at least as high a mean. All risk-averse expected-utility maximizers (that is, those with increasing and concave utility functions) prefer a second-order stochastically dominant gamble to a dominated one. Second-order dominance describes the shared preferences of a smaller class of decision-makers (those for whom more is better and who are averse to risk, rather than all those for whom more is better) than does first-order dominance.

In terms of cumulative distribution functions F A {\displaystyle F_{A}} and F B {\displaystyle F_{B}} , A {\displaystyle A} is second-order stochastically dominant over B {\displaystyle B} if and only if the area under F A {\displaystyle F_{A}} from minus infinity to x {\displaystyle x} is less than or equal to that under F B {\displaystyle F_{B}} from minus infinity to x {\displaystyle x} for all real numbers x {\displaystyle x} , with strict inequality at some x {\displaystyle x} ; that is, x [ F B ( t ) F A ( t ) ] d t 0 {\displaystyle \int _{-\infty }^{x}[F_{B}(t)-F_{A}(t)]\,dt\geq 0} for all x {\displaystyle x} , with strict inequality at some x {\displaystyle x} . Equivalently, A {\displaystyle A} dominates B {\displaystyle B} in the second order if and only if E [ u ( A ) ] E [ u ( B ) ] {\displaystyle \operatorname {E} [u(A)]\geq \operatorname {E} [u(B)]} for all nondecreasing and concave utility functions u ( x ) {\displaystyle u(x)} .

Second-order stochastic dominance can also be expressed as follows: Gamble A {\displaystyle A} second-order stochastically dominates B {\displaystyle B} if and only if there exist some gambles y {\displaystyle y} and z {\displaystyle z} such that x B = d ( x A + y + z ) {\displaystyle x_{B}{\overset {d}{=}}(x_{A}+y+z)} , with y {\displaystyle y} always less than or equal to zero, and with E ( z x A + y ) = 0 {\displaystyle \operatorname {E} (z\mid x_{A}+y)=0} for all values of x A + y {\displaystyle x_{A}+y} . Here the introduction of random variable y {\displaystyle y} makes B {\displaystyle B} first-order stochastically dominated by A {\displaystyle A} (making B {\displaystyle B} disliked by those with an increasing utility function), and the introduction of random variable z {\displaystyle z} introduces a mean-preserving spread in B {\displaystyle B} which is disliked by those with concave utility. Note that if A {\displaystyle A} and B {\displaystyle B} have the same mean (so that the random variable y {\displaystyle y} degenerates to the fixed number 0), then B {\displaystyle B} is a mean-preserving spread of A {\displaystyle A} .

Sufficient conditions for second-order stochastic dominance

  • First-order stochastic dominance of A over B is a sufficient condition for second-order dominance of A over B.
  • If B is a mean-preserving spread of A, then A second-order stochastically dominates B.

Necessary conditions for second-order stochastic dominance

  • E A ( x ) E B ( x ) {\displaystyle \operatorname {E} _{A}(x)\geq \operatorname {E} _{B}(x)} is a necessary condition for A to second-order stochastically dominate B.
  • min A ( x ) min B ( x ) {\displaystyle \min _{A}(x)\geq \min _{B}(x)} is a necessary condition for A to second-order dominate B. The condition implies that the left tail of F B {\displaystyle F_{B}} must be thicker than the left tail of F A {\displaystyle F_{A}} .

Let F A {\displaystyle F_{A}} and F B {\displaystyle F_{B}} be the cumulative distribution functions of two distinct investments A {\displaystyle A} and B {\displaystyle B} . A {\displaystyle A} dominates B {\displaystyle B} in the third order if and only if

  • x z [ F B ( t ) F A ( t ) ] d t d z 0 for all x , {\displaystyle \int _{-\infty }^{x}\int _{-\infty }^{z}[F_{B}(t)-F_{A}(t)]\,dt\,dz\geq 0{\text{ for all }}x,}
  • E A ( x ) E B ( x ) , {\displaystyle \operatorname {E} _{A}(x)\geq \operatorname {E} _{B}(x),\,}

and there is at least one strict inequality. Equivalently, A {\displaystyle A} dominates B {\displaystyle B} in the third order if and only if E A U ( x ) E B U ( x ) {\displaystyle \operatorname {E} _{A}U(x)\geq \operatorname {E} _{B}U(x)} for all nondecreasing, concave utility functions U {\displaystyle U} that are positively skewed (that is, have a positive third derivative throughout).

Sufficient condition

  • Second-order dominance is a sufficient condition.

Necessary conditions

  • E A ( log ( x ) ) E B ( log ( x ) ) {\displaystyle \operatorname {E} _{A}(\log(x))\geq \operatorname {E} _{B}(\log(x))} is a necessary condition. The condition implies that the geometric mean of A {\displaystyle A} must be greater than or equal to the geometric mean of B {\displaystyle B} .
  • min A ( x ) min B ( x ) {\displaystyle \min _{A}(x)\geq \min _{B}(x)} is a necessary condition. The condition implies that the left tail of F B {\displaystyle F_{B}} must be thicker than the left tail of F A {\displaystyle F_{A}} .

Higher orders of stochastic dominance have also been analyzed, as have generalizations of the dual relationship between stochastic dominance orderings and classes of preference functions. Arguably the most powerful dominance criterion relies on the accepted economic assumption of decreasing absolute risk aversion. This involves several analytical challenges and a research effort is on its way to address those.

Stochastic dominance relations may be used as constraints in problems of mathematical optimization, in particular stochastic programming. In a problem of maximizing a real functional f ( X ) {\displaystyle f(X)} over random variables X {\displaystyle X} in a set X 0 {\displaystyle X_{0}} we may additionally require that X {\displaystyle X} stochastically dominates a fixed random benchmark B {\displaystyle B} . In these problems, utility functions play the role of Lagrange multipliers associated with stochastic dominance constraints. Under appropriate conditions, the solution of the problem is also a (possibly local) solution of the problem to maximize f ( X ) + E [ u ( X ) u ( B ) ] {\displaystyle f(X)+\operatorname {E} [u(X)-u(B)]} over X {\displaystyle X} in X 0 {\displaystyle X_{0}} , where u ( x ) {\displaystyle u(x)} is a certain utility function. If the first order stochastic dominance constraint is employed, the utility function u ( x ) {\displaystyle u(x)} is nondecreasing; if the second order stochastic dominance constraint is used, u ( x ) {\displaystyle u(x)} is nondecreasing and concave. A system of linear equations can test whether a given solution if efficient for any such utility function. Third-order stochastic dominance constraints can be dealt with using convex quadratically constrained programming (QCP).

  1. Hadar, J.; Russell, W. (1969). "Rules for Ordering Uncertain Prospects". American Economic Review. 59 (1): 25–34. JSTOR 1811090.
  2. Bawa, Vijay S. (1975). "Optimal Rules for Ordering Uncertain Prospects". Journal of Financial Economics. 2 (1): 95–121. doi:10.1016/0304-405X(75)90025-2.
  3. Quirk, J. P.; Saposnik, R. (1962). "Admissibility and Measurable Utility Functions". Review of Economic Studies. 29 (2): 140–146. doi:10.2307/2295819. JSTOR 2295819.
  4. Hanoch, G.; Levy, H. (1969). "The Efficiency Analysis of Choices Involving Risk". Review of Economic Studies. 36 (3): 335–346. doi:10.2307/2296431. JSTOR 2296431.
  5. Rothschild, M.; Stiglitz, J. E. (1970). "Increasing Risk: I. A Definition". Journal of Economic Theory. 2 (3): 225–243. doi:10.1016/0022-0531(70)90038-4.
  6. Ekern, Steinar (1980). "Increasing Nth Degree Risk". Economics Letters. 6 (4): 329–333. doi:10.1016/0165-1765(80)90005-1.
  7. Vickson, R.G. (1975). "Stochastic Dominance Tests for Decreasing Absolute Risk Aversion. I. Discrete Random Variables". Management Science. 21 (12): 1438–1446. doi:10.1287/mnsc.21.12.1438.
  8. Vickson, R.G. (1977). "Stochastic Dominance Tests for Decreasing Absolute Risk Aversion. II. General random Variables". Management Science. 23 (5): 478–489. doi:10.1287/mnsc.23.5.478.
  9. See, e.g. Post, Th.; Fang, Y.; Kopa, M. (2015). "Linear Tests for DARA Stochastic Dominance". Management Science. 61 (7): 1615–1629. doi:10.1287/mnsc.2014.1960.
  10. Dentcheva, D.; Ruszczyński, A. (2003). "Optimization with Stochastic Dominance Constraints". SIAM Journal on Optimization. 14 (2): 548–566. CiteSeerX10.1.1.201.7815. doi:10.1137/S1052623402420528.
  11. Kuosmanen, T (2004). "Efficient diversification according to stochastic dominance criteria". Management Science. 50 (10): 1390–1406. doi:10.1287/mnsc.1040.0284.
  12. Dentcheva, D.; Ruszczyński, A. (2004). "Semi-Infinite Probabilistic Optimization: First Order Stochastic Dominance Constraints". Optimization. 53 (5–6): 583–601. doi:10.1080/02331930412331327148.
  13. Post, Th (2003). "Empirical tests for stochastic dominance efficiency". Journal of Finance. 58 (5): 1905–1932. doi:10.1111/1540-6261.00592.
  14. Post, Thierry; Kopa, Milos (2016). "Portfolio Choice Based on Third-Degree Stochastic Dominance". Management Science. 63 (10): 3381–3392. doi:10.1287/mnsc.2016.2506. SSRN2687104.

Stochastic dominance
Stochastic dominance Language Watch Edit For other uses see Dominance Stochastic dominance is a partial order between random variables 1 2 It is a form of stochastic ordering The concept arises in decision theory and decision analysis in situations where one gamble a probability distribution over possible outcomes also known as prospects can be ranked as superior to another gamble for a broad class of decision makers It is based on shared preferences regarding sets of possible outcomes and their associated probabilities Only limited knowledge of preferences is required for determining dominance Risk aversion is a factor only in second order stochastic dominance Stochastic dominance does not give a total order but rather only a partial order for some pairs of gambles neither one stochastically dominates the other since different members of the broad class of decision makers will differ regarding which gamble is preferable without them generally being considered to be equally attractive Contents 1 Statewise dominance 2 First order 3 Second order 3 1 Sufficient conditions for second order stochastic dominance 3 2 Necessary conditions for second order stochastic dominance 4 Third order 4 1 Sufficient condition 4 2 Necessary conditions 5 Higher order 6 Constraints 7 See also 8 ReferencesStatewise dominance EditThe simplest case of stochastic dominance is statewise dominance also known as state by state dominance defined as follows Random variable A is statewise dominant over random variable B if A gives at least as good a result in every state every possible set of outcomes and a strictly better result in at least one state For example if a dollar is added to one or more prizes in a lottery the new lottery statewise dominates the old one because it yields a better payout regardless of the specific numbers realized by the lottery Similarly if a risk insurance policy has a lower premium and a better coverage than another policy then with or without damage the outcome is better Anyone who prefers more to less in the standard terminology anyone who has monotonically increasing preferences will always prefer a statewise dominant gamble First order EditStatewise dominance is a special case of the canonical first order stochastic dominance FSD 3 which is defined as Random variable A has first order stochastic dominance over random variable B if for any outcome x A gives at least as high a probability of receiving at least x as does B and for some x A gives a higher probability of receiving at least x In notation form P A x P B x displaystyle P A geq x geq P B geq x for all x and for some x P A x gt P B x displaystyle P A geq x gt P B geq x In terms of the cumulative distribution functions of the two random variables A dominating B means that F A x F B x displaystyle F A x leq F B x for all x with strict inequality at some x Gamble A first order stochastically dominates gamble B if and only if every expected utility maximizer with an increasing utility function prefers gamble A over gamble B First order stochastic dominance can also be expressed as follows If and only if A first order stochastically dominates B there exists some gamble y displaystyle y such that x B d x A y displaystyle x B overset d x A y where y 0 displaystyle y leq 0 in all possible states and strictly negative in at least one state here d displaystyle overset d means is equal in distribution to that is has the same distribution as Thus we can go from the graphed density function of A to that of B by roughly speaking pushing some of the probability mass to the left For example consider a single toss of a fair die with the six possible outcomes states summarized in this table along with the amount won in each state by each of three alternative gambles State die result 1 2 3 4 5 6 Gamble A wins 1 1 2 2 2 2 Gamble B wins 1 1 1 2 2 2 Gamble C wins 3 3 3 1 1 1 displaystyle begin array rcccccc text State die result amp 1 amp 2 amp 3 amp 4 amp 5 amp 6 hline text Gamble A wins amp 1 amp 1 amp 2 amp 2 amp 2 amp 2 text Gamble B wins amp 1 amp 1 amp 1 amp 2 amp 2 amp 2 text Gamble C wins amp 3 amp 3 amp 3 amp 1 amp 1 amp 1 hline end array Here gamble A statewise dominates gamble B because A gives at least as good a yield in all possible states outcomes of the die roll and gives a strictly better yield in one of them state 3 Since A statewise dominates B it also first order dominates B Gamble C does not statewise dominate B because B gives a better yield in states 4 through 6 but C first order stochastically dominates B because Pr B 1 Pr C 1 1 Pr B 2 Pr C 2 3 6 and Pr B 3 0 while Pr C 3 3 6 gt Pr B 3 Gambles A and C cannot be ordered relative to each other on the basis of first order stochastic dominance because Pr A 2 4 6 gt Pr C 2 3 6 while on the other hand Pr C 3 3 6 gt Pr A 3 0 In general although when one gamble first order stochastically dominates a second gamble the expected value of the payoff under the first will be greater than the expected value of the payoff under the second the converse is not true one cannot order lotteries with regard to stochastic dominance simply by comparing the means of their probability distributions For instance in the above example C has a higher mean 2 than does A 5 3 yet C does not first order dominate A Second order EditThe other commonly used type of stochastic dominance is second order stochastic dominance 1 4 5 Roughly speaking for two gambles A displaystyle A and B displaystyle B gamble A displaystyle A has second order stochastic dominance over gamble B displaystyle B if the former is more predictable i e involves less risk and has at least as high a mean All risk averse expected utility maximizers that is those with increasing and concave utility functions prefer a second order stochastically dominant gamble to a dominated one Second order dominance describes the shared preferences of a smaller class of decision makers those for whom more is better and who are averse to risk rather than all those for whom more is better than does first order dominance In terms of cumulative distribution functions F A displaystyle F A and F B displaystyle F B A displaystyle A is second order stochastically dominant over B displaystyle B if and only if the area under F A displaystyle F A from minus infinity to x displaystyle x is less than or equal to that under F B displaystyle F B from minus infinity to x displaystyle x for all real numbers x displaystyle x with strict inequality at some x displaystyle x that is x F B t F A t d t 0 displaystyle int infty x F B t F A t dt geq 0 for all x displaystyle x with strict inequality at some x displaystyle x Equivalently A displaystyle A dominates B displaystyle B in the second order if and only if E u A E u B displaystyle operatorname E u A geq operatorname E u B for all nondecreasing and concave utility functions u x displaystyle u x Second order stochastic dominance can also be expressed as follows Gamble A displaystyle A second order stochastically dominates B displaystyle B if and only if there exist some gambles y displaystyle y and z displaystyle z such that x B d x A y z displaystyle x B overset d x A y z with y displaystyle y always less than or equal to zero and with E z x A y 0 displaystyle operatorname E z mid x A y 0 for all values of x A y displaystyle x A y Here the introduction of random variable y displaystyle y makes B displaystyle B first order stochastically dominated by A displaystyle A making B displaystyle B disliked by those with an increasing utility function and the introduction of random variable z displaystyle z introduces a mean preserving spread in B displaystyle B which is disliked by those with concave utility Note that if A displaystyle A and B displaystyle B have the same mean so that the random variable y displaystyle y degenerates to the fixed number 0 then B displaystyle B is a mean preserving spread of A displaystyle A Sufficient conditions for second order stochastic dominance Edit First order stochastic dominance of A over B is a sufficient condition for second order dominance of A over B If B is a mean preserving spread of A then A second order stochastically dominates B Necessary conditions for second order stochastic dominance Edit E A x E B x displaystyle operatorname E A x geq operatorname E B x is a necessary condition for A to second order stochastically dominate B min A x min B x displaystyle min A x geq min B x is a necessary condition for A to second order dominate B The condition implies that the left tail of F B displaystyle F B must be thicker than the left tail of F A displaystyle F A Third order EditLet F A displaystyle F A and F B displaystyle F B be the cumulative distribution functions of two distinct investments A displaystyle A and B displaystyle B A displaystyle A dominates B displaystyle B in the third order if and only if x z F B t F A t d t d z 0 for all x displaystyle int infty x int infty z F B t F A t dt dz geq 0 text for all x E A x E B x displaystyle operatorname E A x geq operatorname E B x and there is at least one strict inequality Equivalently A displaystyle A dominates B displaystyle B in the third order if and only if E A U x E B U x displaystyle operatorname E A U x geq operatorname E B U x for all nondecreasing concave utility functions U displaystyle U that are positively skewed that is have a positive third derivative throughout Sufficient condition Edit Second order dominance is a sufficient condition Necessary conditions Edit E A log x E B log x displaystyle operatorname E A log x geq operatorname E B log x is a necessary condition The condition implies that the geometric mean of A displaystyle A must be greater than or equal to the geometric mean of B displaystyle B min A x min B x displaystyle min A x geq min B x is a necessary condition The condition implies that the left tail of F B displaystyle F B must be thicker than the left tail of F A displaystyle F A Higher order EditHigher orders of stochastic dominance have also been analyzed as have generalizations of the dual relationship between stochastic dominance orderings and classes of preference functions 6 Arguably the most powerful dominance criterion relies on the accepted economic assumption of decreasing absolute risk aversion 7 8 This involves several analytical challenges and a research effort is on its way to address those 9 Constraints EditStochastic dominance relations may be used as constraints in problems of mathematical optimization in particular stochastic programming 10 11 12 In a problem of maximizing a real functional f X displaystyle f X over random variables X displaystyle X in a set X 0 displaystyle X 0 we may additionally require that X displaystyle X stochastically dominates a fixed random benchmark B displaystyle B In these problems utility functions play the role of Lagrange multipliers associated with stochastic dominance constraints Under appropriate conditions the solution of the problem is also a possibly local solution of the problem to maximize f X E u X u B displaystyle f X operatorname E u X u B over X displaystyle X in X 0 displaystyle X 0 where u x displaystyle u x is a certain utility function If the first order stochastic dominance constraint is employed the utility function u x displaystyle u x is nondecreasing if the second order stochastic dominance constraint is used u x displaystyle u x is nondecreasing and concave A system of linear equations can test whether a given solution if efficient for any such utility function 13 Third order stochastic dominance constraints can be dealt with using convex quadratically constrained programming QCP 14 See also EditModern portfolio theory Marginal conditional stochastic dominance Responsive set extension equivalent to stochastic dominance in the context of preference relations Quantum catalystReferences Edit a b Hadar J Russell W 1969 Rules for Ordering Uncertain Prospects American Economic Review 59 1 25 34 JSTOR 1811090 Bawa Vijay S 1975 Optimal Rules for Ordering Uncertain Prospects Journal of Financial Economics 2 1 95 121 doi 10 1016 0304 405X 75 90025 2 Quirk J P Saposnik R 1962 Admissibility and Measurable Utility Functions Review of Economic Studies 29 2 140 146 doi 10 2307 2295819 JSTOR 2295819 Hanoch G Levy H 1969 The Efficiency Analysis of Choices Involving Risk Review of Economic Studies 36 3 335 346 doi 10 2307 2296431 JSTOR 2296431 Rothschild M Stiglitz J E 1970 Increasing Risk I A Definition Journal of Economic Theory 2 3 225 243 doi 10 1016 0022 0531 70 90038 4 Ekern Steinar 1980 Increasing Nth Degree Risk Economics Letters 6 4 329 333 doi 10 1016 0165 1765 80 90005 1 Vickson R G 1975 Stochastic Dominance Tests for Decreasing Absolute Risk Aversion I Discrete Random Variables Management Science 21 12 1438 1446 doi 10 1287 mnsc 21 12 1438 Vickson R G 1977 Stochastic Dominance Tests for Decreasing Absolute Risk Aversion II General random Variables Management Science 23 5 478 489 doi 10 1287 mnsc 23 5 478 See e g Post Th Fang Y Kopa M 2015 Linear Tests for DARA Stochastic Dominance Management Science 61 7 1615 1629 doi 10 1287 mnsc 2014 1960 Dentcheva D Ruszczynski A 2003 Optimization with Stochastic Dominance Constraints SIAM Journal on Optimization 14 2 548 566 CiteSeerX 10 1 1 201 7815 doi 10 1137 S1052623402420528 Kuosmanen T 2004 Efficient diversification according to stochastic dominance criteria Management Science 50 10 1390 1406 doi 10 1287 mnsc 1040 0284 Dentcheva D Ruszczynski A 2004 Semi Infinite Probabilistic Optimization First Order Stochastic Dominance Constraints Optimization 53 5 6 583 601 doi 10 1080 02331930412331327148 Post Th 2003 Empirical tests for stochastic dominance efficiency Journal of Finance 58 5 1905 1932 doi 10 1111 1540 6261 00592 Post Thierry Kopa Milos 2016 Portfolio Choice Based on Third Degree Stochastic Dominance Management Science 63 10 3381 3392 doi 10 1287 mnsc 2016 2506 SSRN 2687104 Retrieved from https en wikipedia org 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