342 15 A Compactness Principle
Let us defineLn=
∑
kα
k
nH
k. Clearly‖Ln·M‖
H^1 ≤1foreachn.From
Theorem 15.1.3, it follows that there are convex combinations (ηnk)k≥n, dis-
jointly supported, such that
∑
kη
k
n[L
k·M, Lk·M]∞ (^21) converges a.s.. Hence
we have that supn
∑
kη
k
n[L
k·M, Lk·M]∞^12 <∞a.s.. We also may suppose
that maxkηkn→0asn→∞. From Minkowski’s inequality for the bracket it
follows that also supn
[(∑
kη
k
nL
k)·M,(∑
kη
k
nL
k)·M]^12 <∞a.s.. Because
the convex combinations were disjointly supported we also obtain a.s. and for
Rn=
∑
kη
k
n
∑
jα
j
kV
j:
sup
n
[Rn·M, Rn·M]
(^12)
∞≤supn
[(
∑
k
ηknLk
)
·M,
(
∑
k
ηnkLk
)
·M
] (^12)
∞
<∞.
From the fact that the convex combinations were disjointly supported and
from
∑
n^1 En≤d, we conclude that for each point (t, ω)∈R+×Ω, onlyd
vectorsRn(t, ω) can be nonzero. Let us putPn=
∑s=2n+1
s=2n+1^2
−nRs. It follows
that a.s.
∫
Pnd[M, M]Pn≤d
∫
⎛
⎝
s=2∑n+1
s=2n+1
2 −^2 nRsd[M, M]Rs
⎞
⎠
≤d 2 −n
s=2∑n+1
s=2n+1
2 −n[Rs·M, Rs·M]∞
≤d 2 −nsup
s
[Rs·M, Rs·M]∞
→ 0.
If we now putUn=
∑k=2n+1
k=2n+1^2
−n∑
kη
k
l
∑
lα
l
kH
l, we arrive at convex
combinationsUn=
∑
λlnHlsuch that
(1) the convex combinationsλknare disjointly supported,
(2)
(∑
kλ
k
nK
k)·M→H (^0) ·MinH (^1) ,
(3)
[(∑
kλ
k
nV
k)·M,(∑
kλ
k
nV
k)·M]
∞→0 in probability,
(4)
[(∑
kλ
k
nW
k)·M,(∑
kλ
k
nW
k)·M]
∞→0 in probability, and even
(5)
((∑
kλ
k
nW
k)·M)∗→0 in probability.
As a consequence we obtain that
[(
Un−H^0
)
·M,
(
Un−H^0
)
·M
]
∞→0in
probability, and hence inLp(Ω,F,P)foreachp<1.
We remark that these properties will remain valid if we take once more
convex combinations of the predictable processes(∑ Un. The stochastic integrals
kλ
k
nV
k)·M need not converge in the semi-martingale topology as the
example in Sect. 15.3 shows. But exactly as in the Subsect. 15.4.2 we will show
that after taking once more convex combinations, they converge in a pointwise
sense, to a process of finite variation.