In financial mathematics and economics, a distortion risk measure is a type of risk measure which is related to the cumulative distribution function of the return of a financial portfolio.
The function associated with the distortion function is a distortion risk measure if for any random variable of gains (where is the Lp space) then
where is the cumulative distribution function for and is the dual distortion function .
If almost surely then is given by the Choquet integral, i.e. Equivalently, such that is the probability measure generated by , i.e. for any the sigma-algebra then .
In addition to the properties of general risk measures, distortion risk measures also have:
Law invariant: If the distribution of and are the same then .
Monotone with respect to first order stochastic dominance.
If is a concave distortion function, then is monotone with respect to second order stochastic dominance.
is a concave distortion function if and only if is a coherent risk measure.
Value at risk is a distortion risk measure with associated distortion function
Conditional value at risk is a distortion risk measure with associated distortion function
The negative expectation is a distortion risk measure with associated distortion function .
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In financial mathematics, a risk measure is used to determine the amount of an asset or set of assets (traditionally currency) to be kept in reserve. The purpose of this reserve is to make the risks taken by financial institutions, such as banks and insurance companies, acceptable to the regulator. In recent years attention has turned towards convex and coherent risk measurement. A risk measure is defined as a mapping from a set of random variables to the real numbers. This set of random variables represents portfolio returns.
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