Английская Википедия: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

Given a probability space <math>(\Omega,\mathcal{F},\mathbb{P})</math>, and letting <math>L^p = L^p(\Omega,\mathcal{F},\mathbb{P})</math> be the Lp space in the scalar case and <math>L_d^p = L_d^p(\Omega,\mathcal{F},\mathbb{P})</math> in d-dimensions, then we can define acceptance sets as below.

Scalar Case

An acceptance set is a set <math>A</math> satisfying:

1. <math>A \supseteq L^p_+</math>
2. <math>A \cap L^p_{--} = \emptyset</math> such that <math>L^p_{--} = \{X \in L^p: \forall \omega \in \Omega, X(\omega) < 0\}</math>
3. <math> A \cap L^p_- = \{0\}</math>
4. Additionally if <math>A</math> is convex then it is a convex acceptance set
   1. And if <math>A</math> is a positively homogeneous cone then it is a coherent acceptance set

Set-valued Case

An acceptance set (in a space with <math>d</math> assets) is a set <math>A \subseteq L^p_d</math> satisfying:

1. <math>u \in K_M \Rightarrow u1 \in A</math> with <math>1</math> denoting the random variable that is constantly 1 <math>\mathbb{P}</math>-a.s.
2. <math> u \in -\mathrm{int}K_M \Rightarrow u1 \not\in A</math>
3. <math>A</math> is directionally closed in <math>M</math> with <math>A + u1 \subseteq A \; \forall u \in K_M</math>
4. <math>A + L^p_d(K) \subseteq A</math>

Additionally, if <math>A</math> is convex (a convex cone) then it is called a convex (coherent) acceptance set.

Note that <math>K_M = K \cap M</math> where <math>K</math> is a constant solvency cone and <math>M</math> is the set of portfolios of the <math>m</math> reference assets.

Relation to Risk Measures

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 <math>R_{A_R}(X) = R(X)</math> and <math>A_{R_A} = A</math>.Шаблон:Citation needed

Risk Measure to Acceptance Set

• If <math>\rho</math> is a (scalar) risk measure then <math>A_{\rho} = \{X \in L^p: \rho(X) \leq 0\}</math> is an acceptance set.
• If <math>R</math> is a set-valued risk measure then <math>A_R = \{X \in L^p_d: 0 \in R(X)\}</math> is an acceptance set.

Acceptance Set to Risk Measure

• If <math>A</math> is an acceptance set (in 1-d) then <math>\rho_A(X) = \inf\{u \in \mathbb{R}: X + u1 \in A\}</math> defines a (scalar) risk measure.
• If <math>A</math> is an acceptance set then <math>R_A(X) = \{u \in M: X + u1 \in A\}</math> is a set-valued risk measure.

Examples

Superhedging price

Шаблон:Main 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

<math>A = \{-V_T: (V_t)_{t=0}^T \text{ is the price of a self-financing portfolio at each time}\}</math>.

Entropic risk measure

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

<math>A = \{X \in L^p(\mathcal{F}): E[u(X)] \geq 0\} = \{X \in L^p(\mathcal{F}): E\left[e^{-\theta X}\right] \leq 1\}</math>

where <math>u(X)</math> is the exponential utility function.