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

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

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

Properties

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

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

Examples

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

References

1 2 3 4 Sereda, E. N.; Bronshtein, E. M.; Rachev, S. T.; Fabozzi, F. J.; Sun, W.; Stoyanov, S. V. (2010). "Distortion Risk Measures in Portfolio Optimization". Handbook of Portfolio Construction. p. 649. doi:10.1007/978-0-387-77439-8_25. ISBN 978-0-387-77438-1.
1 2 3 4 5 Julia L. Wirch; Mary R. Hardy. "Distortion Risk Measures: Coherence and Stochastic Dominance" (pdf). Retrieved March 10, 2012.
1 2 3 Balbás, A.; Garrido, J.; Mayoral, S. (2008). "Properties of Distortion Risk Measures". Methodology and Computing in Applied Probability. 11 (3): 385. doi:10.1007/s11009-008-9089-z.

Wu, Xianyi; Xian Zhou (April 7, 2006). "A new characterization of distortion premiums via countable additivity for comonotonic risks". Insurance: Mathematics and Economics. 38 (2): 324–334. doi:10.1016/j.insmatheco.2005.09.002. Retrieved March 14, 2012.

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Distortion risk measure

Mathematical definition

Properties

Examples

See also

References