5.2 No Free Lunch 81
reasoning. However, both arguments amount to the same, and the difference
is only superficial.
Fix 1≤p≤∞,and1≤q≤∞such that^1 p+^1 q =1,andletE =
Lp(Ω,F,P),E′=Lq(Ω,F,P). We denote byE+={f∈Lp|f≥0a.s.}
(resp.E− ={f∈Lp|f ≤0a.s.}) the cone of non-negative (resp. non-
positive) random variables inLp.
Theorem 5.2.3.With the above notation letC ⊂Ebe a σ(E, E′)-closed
convex cone containingE−and suppose thatC∩E+={ 0 }. Then there is
aprobabilitymeasureQonF, which is equivalent toP, satisfying ddQP∈E′,
and so that, for allf∈C, we haveEQ[f]≤ 0.
Conversely, given a probability measureQonF, equivalent toPand sat-
isfyingddQP∈E′,theconeC={f∈E|EQ[f]≤ 0 }isσ(E, E′)-closed and
satisfiesC∩E+={ 0 }.
Proof.For 1≥δ>0, letBδbe the convex set inE
Bδ={f∈E| 0 ≤f≤ 1 ,E[f]≥δ}.
ClearlyBδisσ(E, E′)-compact andC∩Bδ=∅. The Hahn-Banach The-
orem in its version as separating hyperplane (see, e.g., [Sch 99]) gives the
existence of an elementgδ∈E′so that
sup
f∈C
E[gδf]<min
h∈Bδ
E[gδh]. (5.2)
BecauseCis a cone this means that for allf∈Cwe must haveE[gδf]≤0.
This implies that the sup on the left hand side of (5.2) equals 0, so that the
min on the right hand side must be strictly positive. SinceCcontainsE−
we must havegδ≥0 almost surely. Nowgδcannot be identically zero since
I1∈Bδand henceE[gδ1]I>0. We may normalisegδto haveE[gδ]=1.
For eachn≥1weconsiderδn =2−nand letQnbe the probability
measure defined asddQPn=g 2 −n. For later use let us denote byαnthe number
αn=‖g 2 −n‖E′∈[1,∞[. Also remark that forA∈FwithP[A]> 2 −n,we
must haveQn[A]=E[ (^1) Ag 2 −n]>0.
The elementQis now defined as
Q=
∑∞
n=1
βnQn,
wherewechoosetheweightsβn>0 such that
∑∞
n=1βn=1and
∑∞
n=1βnαn<
∞. For instance we could takeβn=^2
−n
cαn wherec=
∑∞
n=1
2 −n
αn <∞.
The probability measureQsatisfies
(i) for allA,P[A]>0wehaveQ[A]=
∑∞
n=1βnQn[A]>0.
(ii) ddQP∈E′since
∥
∥
∥ddQP
∥
∥
∥
E′
≤
∑∞
n=1βnαn<∞.