# Distortion risk measure

In financial mathematics, 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.

## Mathematical definition

The function ${\displaystyle \rho _{g}:L^{p}\to \mathbb {R} }$ associated with the distortion function ${\displaystyle g:[0,1]\to [0,1]}$ is a distortion risk measure if for any random variable of gains ${\displaystyle X\in L^{p}}$ (where ${\displaystyle L^{p}}$ is the Lp space) then

${\displaystyle \rho _{g}(X)=-\int _{0}^{1}F_{-X}^{-1}(p)d{\tilde {g}}(p)=\int _{-\infty }^{0}{\tilde {g}}(F_{-X}(x))dx-\int _{0}^{\infty }g(1-F_{-X}(x))dx}$

where ${\displaystyle F_{-X}}$ is the cumulative distribution function for ${\displaystyle -X}$ and ${\displaystyle {\tilde {g}}}$ is the dual distortion function ${\displaystyle {\tilde {g}}(u)=1-g(1-u)}$.[1]

If ${\displaystyle X\leq 0}$ almost surely then ${\displaystyle \rho _{g}}$ is given by the Choquet integral, i.e. ${\displaystyle \rho _{g}(X)=-\int _{0}^{\infty }g(1-F_{-X}(x))dx.}$[1][2] Equivalently, ${\displaystyle \rho _{g}(X)=\mathbb {E} ^{\mathbb {Q} }[-X]}$[2] such that ${\displaystyle \mathbb {Q} }$ is the probability measure generated by ${\displaystyle g}$, i.e. for any ${\displaystyle A\in {\mathcal {F}}}$ the sigma-algebra then ${\displaystyle \mathbb {Q} (A)=g(\mathbb {P} (A))}$.[3]

### Properties

In addition to the properties of general risk measures, distortion risk measures also have:

1. Law invariant: If the distribution of ${\displaystyle X}$ and ${\displaystyle Y}$ are the same then ${\displaystyle \rho _{g}(X)=\rho _{g}(Y)}$.
2. Monotone with respect to first order stochastic dominance.
1. If ${\displaystyle g}$ is a concave distortion function, then ${\displaystyle \rho _{g}}$ is monotone with respect to second order stochastic dominance.
3. ${\displaystyle g}$ is a concave distortion function if and only if ${\displaystyle \rho _{g}}$ is a coherent risk measure.[1][2]

## Examples

• Value at risk is a distortion risk measure with associated distortion function ${\displaystyle g(x)={\begin{cases}0&{\text{if }}0\leq x<1-\alpha \\1&{\text{if }}1-\alpha \leq x\leq 1\end{cases}}.}$[2][3]
• Conditional value at risk is a distortion risk measure with associated distortion function ${\displaystyle g(x)={\begin{cases}{\frac {x}{1-\alpha }}&{\text{if }}0\leq x<1-\alpha \\1&{\text{if }}1-\alpha \leq x\leq 1\end{cases}}.}$[2][3]
• The negative expectation is a distortion risk measure with associated distortion function ${\displaystyle g(x)=x}$.[1]