Distortion risk measure

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Template:Short description 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.

Mathematical definition

The function ρg:Lp→ℝ associated with the distortion function g:[0,1]→[0,1] is a distortion risk measure if for any random variable of gains X∈Lp (where Lp is the Lp space) then

ρg(X)=−∫01F−X−1(p)dg~(p)=∫−∞0g~(F−X(x))dx−∫0∞g(1−F−X(x))dx

where F−X is the cumulative distribution function for −X and g~ is the dual distortion function g~(u)=1−g(1−u).[1]

If X≤0 almost surely then ρg is given by the Choquet integral, i.e. ρg(X)=−∫0∞g(1−F−X(x))dx.[1][2] Equivalently, ρg(X)=𝔼ℚ[−X][2] such that ℚ is the monotone and normalized set function generated by g, i.e. for any A∈ℱ the sigma-algebra then ℚ(A)=g(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 X and Y are the same then ρg(X)=ρg(Y).
  2. Monotone with respect to first order stochastic dominance.
    1. If g is a concave distortion function, then ρg is monotone with respect to second order stochastic dominance.
  3. g is a concave distortion function if and only if ρg is a coherent risk measure.[1][2]

Examples

  • Value at risk is a distortion risk measure with associated distortion function g(x)={0if 0≤x<1−α1if 1−α≤x≤1.[2][3]
  • Conditional value at risk is a distortion risk measure with associated distortion function g(x)={x1−αif 0≤x<1−α1if 1−α≤x≤1.[2][3]
  • The negative expectation is a distortion risk measure with associated distortion function g(x)=x.[1]

See also

References

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