In this paper we provide a novel family of stochastic orders  , which we call the \alpha,[a,b]-concave stochastic orders , that generalizes second order stochastic dominance . These stochastic orders are generated by a novel set of "very" concave functions where \alpha parameterizes the degree of concavity. The \alpha,[a,b]-concave stochastic orders allow us to derive novel comparative statics results for important applications in economics that could not be derived using previous stochastic orders. In particular, our comparative statics results are useful when an increase in the lottery's riskiness increases the agent's optimal action , but an increase in the lottery's expected value decreases the agent's optimal action . For this kind of situation, we provide a tool to determine which of these two forces dominates -- riskiness or expected value. We apply our results in consumption-savings problems, self-protection problems, and in a Bayesian game.
In this paper we provide a novel family of stochastic orders that generalizes second order stochastic dominance , which we call the \alpha,[a,b]-concave stochastic orders . These stochastic orders are generated by a novel set of "very" concave functions where \alpha parameterizes the degree of concavity. The \alpha,[a,b]-concave stochastic orders allow us to derive novel comparative statics results for important applications in economics that cannot be derived using previous stochastic orders. In particular, our comparative statics results are useful when an increase in a lottery's riskiness changes the agent's optimal action in the opposite direction to an increase in the lottery's expected value . For this kind of situation, we provide a tool to determine which of these two forces dominates -- riskiness or expected value. We apply our results in consumption-savings problems, self-protection problems, and in a Bayesian game.
A
that generalizes second order stochastic dominance
D
, that generalizes second order stochastic dominance
R
could not
cannot
R
the
a
R
increases
changes
R
, but
in the opposite direction to
D
decreases the agent's optimal action