Distortion risk measure

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

ρg(X)=01FX1(p)dg~(p)=0g~(FX(x))dx0g(1FX(x))dx

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

If X0 almost surely then ρg is given by the Choquet integral, i.e. ρg(X)=0g(1FX(x))dx.[1][2] Equivalently, ρg(X)=𝔼[X][2] such that is the probability measure generated by g, i.e. for any A the sigma-algebra then (A)=g((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 0x<1α1if 1αx1.[2][3]
  • Conditional value at risk is a distortion risk measure with associated distortion function g(x)={x1αif 0x<1α1if 1αx1.[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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