Acceptance set

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In financial mathematics, acceptance set is a set of acceptable future net worth which is acceptable to the regulator. It is related to risk measures.

Mathematical Definition

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Given a probability space (Ω,,), and letting Lp=Lp(Ω,,) be the Lp space in the scalar case and Ldp=Ldp(Ω,,) in d-dimensions, then we can define acceptance sets as below.

Scalar Case

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An acceptance set is a set A satisfying:

  1. AL+p
  2. ALp= such that Lp={XLp:ωΩ,X(ω)<0}
  3. ALp={0}
  4. Additionally if A is convex then it is a convex acceptance set
    1. And if A is a positively homogeneous cone then it is a coherent acceptance set[1]

Set-valued Case

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An acceptance set (in a space with d assets) is a set ALdp satisfying:

  1. uKMu1A with 1 denoting the random variable that is constantly 1 -a.s.
  2. uintKMu1∉A
  3. A is directionally closed in M with A+u1AuKM
  4. A+Ldp(K)A

Additionally, if A is convex (a convex cone) then it is called a convex (coherent) acceptance set. [2]

Note that KM=KM where K is a constant solvency cone and M is the set of portfolios of the m reference assets.

Relation to Risk Measures

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An acceptance set is convex (coherent) if and only if the corresponding risk measure is convex (coherent). As defined below it can be shown that RAR(X)=R(X) and ARA=A.[citation needed]

Risk Measure to Acceptance Set

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  • If ρ is a (scalar) risk measure then Aρ={XLp:ρ(X)0} is an acceptance set.
  • If R is a set-valued risk measure then AR={XLdp:0R(X)} is an acceptance set.

Acceptance Set to Risk Measure

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  • If A is an acceptance set (in 1-d) then ρA(X)=inf{u:X+u1A} defines a (scalar) risk measure.
  • If A is an acceptance set then RA(X)={uM:X+u1A} is a set-valued risk measure.

Examples

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Superhedging price

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The acceptance set associated with the superhedging price is the negative of the set of values of a self-financing portfolio at the terminal time. That is

A={VT:(Vt)t=0T is the price of a self-financing portfolio at each time}.

Entropic risk measure

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The acceptance set associated with the entropic risk measure is the set of payoffs with positive expected utility. That is

A={XLp():E[u(X)]0}={XLp():E[eθX]1}

where u(X) is the exponential utility function.[3]

References

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  1. ^ Lua error in Module:Citation/CS1/Configuration at line 2172: attempt to index field '?' (a nil value).
  2. ^ Lua error in Module:Citation/CS1/Configuration at line 2172: attempt to index field '?' (a nil value).
  3. ^ Lua error in Module:Citation/CS1/Configuration at line 2172: attempt to index field '?' (a nil value).