On s-additive robust representation of convex risk measures for unbounded financial positions in the presence of uncertainty about the market model



n)n Q. Then for arbitrary (λn)n Q, m N and every ω Ω

m∞         m                m

X1(ω)(1 - Xλn) ≤ X λnXn(ω) - XλnXn(ω) ≤ X(ω)(1 - Xλn).
n=1      n=1            n=1                      n=1

This implies

∞m

(*) lim Λ(P λnXn) -Λ(P λnXn) =0 for anyΛ βρ-1(R).
m→∞   n=1          n=1

Let us define ∆c := {αρ ≤ c}. Then in view of Lemma 6.1 and statement .1 there exists some real c* > —ρ(0) with

ρ(Y) =   max  (-Λ(Y) - βρ(Λ)) = max (-EP [Y] - αρ(P))

Λ{βρc*}                        pδc*

for Y {X, P λnXn | (λn )n Q}.
n=1

The sequence (fn)n in R{βρc }, defined by fn(Λ) = —Λ(Xn) — βρ(Λ), is uniformly bounded because

-2c - p(-X) ≤ βρ(Λ.) - p(-X) -βρ(Λ.) ≤ fn (Λ.) ≤ ρ(Xn) ρ(Xι).

Furthermore for (λn)n Q the mapping    λnfn is well defined with

n=1
∞                m           m∞

X λnfn(Λ) = lim (—Λ(X λnXn) — βρ(Λ) X λn) = —Λ(X λnXn) — βρ(Λ)
m→∞
n=1                      n=1               n=1           n=1

for βρ (Λ) ≤ c* due to (*). Hence
∞∞    ∞     ∞

sup X λnfn (Λ) = ρ(X λnXn) = max (—Ep^X λnXn] — αp (P)) = max X λnfn(EpX).

Л^вр^*} n = 1              n = 1          pδc*       n=ι                    pδc* n = ι

The application of Simons’ lemma (cf. [20], Lemma 2) and the monotone convergence theorem leads then to

ρ(X) = sup (—EP[X] — αρ(P)) = sup limsupfn(EPX) ≥ inf{   sup f(Λ) f co({fn n N})},

Pc.                        Pc.    n                      Λρ≤c*}

where co({fn n N}) denotes the convex hull of {fn n N} in R{βρc }. Thus for ε > 0 there is some convex
rr

combination f =    λifni with n1 < ... < nr and ρ(X) + ε > sup   f(Λ) = ρ(   λifni ). In particular the

i=1                                                    Λρ≤c*}             i=1

inequalities ρ(X) + ε > ρ(Xn ) ≥ ρ(X) hold for n ≥ nr , which implies lim ρ(Xn ) = ρ(X).
n→∞

proof of .4 .3:

Let X+b gather all nonnegative X Xb . Drawing on Corollary 3.3 it suffices to prove that every linear form
Λ from the domain of β
ρ is representable by a probability measure. So let Λ belong to βρ-1 (R). Then by part
of statement .4 as well as Proposition 6.6, the linear form Λ is representable by some probability content Q on
σ(
X) in the sense as introduced just before Lemma 6.5. We want to apply Proposition A.1 (cf. appendix A) to
∞               ∞ ∞               -1

verify that Q is a probability measure. Since Ω\ T Xn1([xn, ∞[) = S S (xn — (Xn Xn))  ([m1, ∞[) holds

n=1                  n=1 m=1

for every pair (Xn )n , (xn)n of sequences in L X+b and ]0, ∞[ respectively, it remains to show by assumption

22



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