Superquantiles and Their Applications to Risk, Random Variables, and Regression
Abstract
Superquantiles (also called conditional values-at-risk) are useful tools in risk modeling
and optimization, with expanding roles beyond these areas. This tutorial provides
a broad overview of superquantiles and their versatile applications. We see that
superquantiles are as fundamental to the description of a random variable as the
cumulative distribution function (cdf), they can recover the corresponding quantile
function through differentiation, they are dual in some sense to superexpectations,
which are convex functions uniquely defining the cdf, and they also characterize convergence
in distribution. A superdistribution function defined by superquantiles leads
to higher-order superquantiles as well as new measures of risk and error, with important
applications in risk modeling and generalized regression.
Description
The article of record as published may be found at http://dx.doi.org/10.1287 /educ.2013.0lll
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.Collections
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