Let (Ω,F, P) be a probability space with right-continuous filtrations (Ft)t≥0
and (Ht)t≥0. Moreover, let F∞=W
t≥0Ft and H∞=W
t≥0Ht. Our objective is to study the enlarged filtration
Gt=\
s>t
(Fs∨ Hs), t≥0.
We relate this enlargement to a measure change on the product space Ω = Ω¯ ×Ω
equipped with the σ-field
F¯=F∞⊗ H∞. We endow ¯Ω with the filtration
F¯t=\
s>t
(Fs⊗ Hs), t≥0.
Ω will be embedded into ¯Ω by the map
ψ : (Ω,F)→( ¯Ω,F¯), ω7→(ω, ω).
We denote by ¯P the image of the measure P under ψ, i.e.
P¯ =Pψ. 8
Hence for all ¯F-measurable functions f : ¯Ω→R we have Z
f(ω, ω0)dP¯(ω, ω0) = Z
f(ω, ω)dP(ω). (2.1) We use notations and concepts of stochastic analysis as explained in the book by Protter [41]. Most of our results only hold for completed filtrations. Since we consider different measures relative to which completions are taken, we use the following notation. Let (Kt) be a filtration and Ra probability measure.
We denote by (KRt ) the filtration (Kt) completed by the R-negligible sets.
We start with a simple observation.
Lemma 2.1.1. If f¯ : ¯Ω → R is F¯tP¯-measurable, then the map f¯◦ ψ is GtP-measurable.
Proof.First observe that Gt = \
s>t
σ(A∩B :A∈ Fs, B ∈ Hs)
= \
s>t
σ ψ−1(A×B) :A∈ Fs, B ∈ Hs
= ψ−1 \
s>t
(Fs⊗ Hs)
!
=ψ−1( ¯Ft).
Now let ¯f = 1AwithA∈F¯tP¯. There is a setB ∈F¯tsuch that ¯P(A4B) = 0.
From the first part we deduce that the map 1B◦ψ is Gt-measurable. Since we have P-almost surely
1A◦ψ = 1B◦ψ,
the map 1A◦ψ is GtP-measurable. By standard arguments one can show the statement for arbitrary ¯FtP¯-measurable functions.
Lemma 2.1.2. If X¯ is ( ¯FtP¯)-predictable, then X¯ ◦ψ is (GtP)-predictable.
Proof.Let 0< s≤t, A∈F¯sP¯ and θ¯= 1A1]s,t]
Then, by Lemma 2.1.1, ¯θ◦ψ = (1A◦ψ)1]s,t] is (GtP)-predictable. The proof
may be completed by a monotone class argument.
10 Lemma 2.1.3. Let Y¯ be ( ¯FtP¯)-adapted. Then the process
Y = ¯Y ◦ψ
is (GtP)-adapted. Moreover, if Y¯ is a ( ¯FtP¯,P¯)-local martingale, then Y is a (GtP, P)-local martingale.
Proof.The first statement follows immediately from Lemma 2.1.1. Now suppose that ¯Y is a ( ¯FtP¯,P¯)-martingale. Let 0 ≤ s < t and A ∈ Gs. Then there is a set B ∈F¯s such that ψ−1(B) =A and hence
EP[1A(Yt−Ys)] = EP¯[1B( ¯Yt−Y¯s)] = 0.
Thus Y is a (GtP)-martingale.
Finally, let ¯Y be a ( ¯FtP¯)-local martingale and ¯T a localizing stopping time.
The random time T = ¯T ◦ψ is a (GtP)-stopping time, since {T ≤t}=ψ−1{T¯≤t} ∈ψ−1( ¯FtP¯)⊂ GtP.
Now it is straightforward to show that Y is a (GtP)-local martingale.
Theorem 2.1.4. Let Y¯ be a( ¯FtP¯,P¯)-semimartingale. Then the processY = Y¯ ◦ψ is a (GtP, P)-semimartingale.
Proof.Let ¯Y be a ( ¯FtP¯)-semimartingale and Y = ¯Y ◦ψ. ObviouslyY has cadlag pathsP-a.s. and Lemma 2.1.3 implies thatY is (GtP)-adapted. By the theorem of Bichteler-Dellacherie-Mokobodzki it is sufficient to show that if (θn) is a sequence of simple (Gt)-adapted integrands converging uniformly to 0, then the simple integrals (θn·Y) converge to 0 in probability relative toP. Recall that any (Gt)-simple integrand is of the formP
1≤i≤n1]ti,ti+1]θi,where θi is Gti-measurable. Since Gt = ψ−1( ¯Ft), one can find simple ( ¯Ft)-adapted processes (¯θn) converging uniformly to 0 such that ¯θn◦ψ =θn. The process Y¯ being a semimartingale implies that the sequence (¯θn·Y¯) converges to 0 in probability relative to ¯P, and hence (θn·Y) converges to 0 in probability
relative to P.
So far we have seen how objects can be translated from ¯Ω to Ω. Now we look at the reverse transfer. For this we may use any product measure on ¯Ω:
let R be a probability measure on H∞, and Q¯=P
F
∞⊗R H
∞. We will sometimes denote ¯Q asdecoupling measure.
Lemma 2.1.5. LetM be a right-continuous (FtP, P)-local martingale. Then the process M¯(ω, ω0) =M(ω) is a ( ¯FtQ¯,Q)-local martingale.¯
Proof.It is immediate that ¯M is ( ¯FtQ¯)-adapted. Assume at first thatM is a strict (FtP, P)-local martingale. Then, for 0≤s < t, and A∈ Fs, B ∈ Hs we have
EQ¯[1A(ω)1B(ω0)( ¯Mt−M¯s)] =R(B)EP[1A(Mt−Ms)] = 0.
By the monotone class theorem, for all bounded (Fs⊗ Hs)-measurable func-tions θ we have
EQ¯[θ( ¯Mt−M¯s)] = 0.
Since ¯M is right-continuous, this remains true for all boundedT
u>s(Fu⊗Hu )-measurable θ, and hence ¯M is a martingale with respect to ( ¯FtQ¯).
Via ¯T(ω, ω0) =T(ω) stopping times can be trivially extended to the prod-uct space. This finally shows that the local martingale property translates
to ¯Ω with respect to ¯Q.
In the sequel we will always assume that ¯P is absolutely continuous with respect to ¯Q, i.e.
Assumption 2.1.6.
P¯ Q¯ on F¯.
Note that this assumption is always satisfied ifR∼P and (Gt) is obtained by an initial enlargement by some discrete random variableG, i.e.Ht=σ(G) for all t ≥0.
Now let M be a (FtP, P)-local martingale and ¯M its extension to ¯Ω as in Lemma 2.1.5. Since ¯P Q, ¯¯ M is a ( ¯FtP¯,P¯)-semimartingale and hence, by Theorem 2.1.4, M is a (GtP, P)-semimartingale. Thus, clearly hypothesis (H’) is satisfied. But what is the Doob-Meyer decomposition of M relative to (GtP, P)?
Essentially the change of filtrations corresponds to changing the measure from ¯Qto ¯P on the product space ¯Ω. Girsanov’s theorem applies on ¯Ω, since the measure ¯P is absolutely continuous with respect to ¯Q. As a consequence we obtain a Girsanov-type result for the corresponding change of filtrations.
For its explicit description we introduce the density process. Let ( ¯Zt) denote a cadlag ( ¯FtQ¯)-adapted process with
Z¯t = dP¯ dQ¯ F¯Q¯
t
.
12 Note that we need to consider the completed filtration in order to assure the existence of a cadlag density process. Theorem 2.1.4 implies that the process Z defined by
Z = ¯Z◦ψ
is a (GtP, P)-semimartingale. Before giving the Girsanov-type results, we show how the quadratic variation processes behave under the projection ψ.
Lemma 2.1.7. Let X¯ andY¯ be( ¯FtP¯,P¯)-semimartingales. IfX = ¯X◦ψ and Y = ¯Y ◦ψ, then
[ ¯X,Y¯]◦ψ = [X, Y] up to indistinguishability relative to P.
Proof.Put X = ¯X◦ψ and Y = ¯Y ◦ψ. Let t > 0 and tni = t2in for all i= 0,1, . . . ,2n. It is known that the sums
X¯0Y¯0+ X
0≤i<2n
( ¯Xtn
i+1−X¯tn
i)( ¯Ytn
i+1 −Y¯tn
i)
converge to [ ¯X,Y¯]t in probability relative to ¯P (see Theorem 20, Chapter VIII in [14]). Hence [ ¯X,Y¯]t◦ψ is the limit (in probability) of the sums
X0Y0+ X
0≤i<2n
(Xtni+1−Xtni)(Ytni+1 −Ytni)
relative to P. Obviously the limit is also equal to [X, Y]t, and hence we have [ ¯X,Y¯]t◦ψ = [X, Y]t.
Since both processes are cadlag, they coincide up to indistinguishability
rel-ative to P.
Let ¯M be a (FQ¯,Q)-semimartingale and¯ M = ¯M ◦ψ. Since ¯P is ab-solutely continuous with respect to ¯Q, ¯M is also a ( ¯FtP¯,P¯)-semimartingale.
Moreover, the bracket [ ¯M ,Z] relative to ¯¯ Q is ¯P-indistinguishable from the bracket relative to ¯P. Similarly, Lemma 2.1.7 implies that the bracket [M, Z]
of the (GtP, P)-semimartingales M and Z coincides with [ ¯M ,Z¯]◦ψ.
We are now in a position to state the first Girsanov-type result. We begin with some definitions. Let
T¯= inf{t >0 : ¯Zt= 0,Z¯t− >0}
and ¯Ut = ∆ ¯MT¯1{t≥T¯}. We further denote by ˜U the compensator of ¯U, i.e. the ( ¯FtQ¯,Q)-predictable projection of ¯¯ U. Moreover, we will use the abbreviation Uˆ = ˜U ◦ψ.
Theorem 2.1.8. If M is a (FtP, P)-local martingale with M0 = 0, then M − 1
Z ·[M, Z] + ˆU (2.2)
is a (GtP, P)-local martingale.
Proof.Let M be an (FtP, P)-local martingale with M0 = 0. We may assume that M has cadlag paths. Lemma 2.1.5 implies that the process defined by
M¯(ω, ω0) =M(ω)
is a ( ¯FtQ¯)-local martingale and the Lenglart-Girsanov Theorem yields that M¯ − 1
Z¯ ·[ ¯M ,Z¯] + ˜Ut
is a ( ¯FtP¯,P¯)-local martingale (see Th´eor`eme 3 in [36] or Chapter III in [41]).
Since the bracket process [ ¯M ,Z]¯ ◦ψ is P-indistinguishable from [M, Z] (see Lemma 2.1.7), we have
(1
Z¯ ·[ ¯M ,Z])¯ ◦ψ = 1
Z ·[M, Z]
up to indistinguishability. With Lemma 2.1.3 we conclude that M− 1
Z ·[M, Z] + ˆUt
is a (GtP, P)-local martingale.
In caseM is continuous, the preceding decomposition in the larger filtra-tion simplifies.
Theorem 2.1.9. IfM is a continuous(FtP, P)-local martingale withM0 = 0, then
M − 1
Z ·[M, Z]
is a (GtP, P)-local martingale.
Proof.LetM be a continuous (FtP, P)-local martingale withM0 = 0 and put ¯M(ω, ω0) = M(ω). The related process ¯U vanishes, and hence ˆU vanishes as well. The result follows now from Theorem 2.1.8.
14 The preceding may also be formulated in terms of the stochastic logarithm of the density process ¯Z. To this end set ¯S = inf{t >0 : ¯Zt = 0,∆ ¯Zt = 0}
and define
L¯ = Z ·
0+
1 Z¯−
dZ¯ on [0,S[.¯ (2.3)
So far, the process ¯L is determined ¯P-, but not Q-almost everywhere. (In¯ order to define it everywhere we may put ¯L = 0 on [ ¯S,∞[.) Then ¯L is an ( ¯FtP¯,P¯)-semimartingale but not necessarily an ( ¯FtQ¯,Q)-semimartingale.¯ However, restricted to the time interval [0,S[ it is an ( ¯¯ FtQ¯,Q)-local martin-¯ gale. As usual we write L= ¯L◦ψ. Alternatively, one can define L through the stochastic integral
L= Z ·
0+
1 Z−
dZ.
Since the process ¯L is a ( ¯FtQ¯,Q)-local martingale on the interval [0,¯ S[, it¯ can be decomposed into a unique local-martingale part ¯Lc and a sum of compensated jumps ¯Ld. As before, we consider the processes Lc = ¯Lc ◦ψ and Ld = ¯Ld◦ψ.
Theorem 2.1.9 can now be reformulated as follows.
Theorem 2.1.10. IfM is a continuous(FtP, P)-local martingale withM0 = 0, then
M−[M, L]
is a (GtP, P)-local martingale.
Proof.Let M be a continuous (FtP, P)-local martingale with M0 = 0.
SinceM is continuous, the bracket process [M, Z] is continuous and Theorem 2.1.9 implies that
M − 1
Z ·[M, Z] =M − 1 Z−
·[M, Z]
is a (GtP, P)-local martingale. Moreover, the definition of L implies that
1
Z−·[M, Z] = [M, L],P-a.s., so thatM−[M, L] is a (GtP, P)-local martingale.
Finally, we will need the following formula, in which the subtracted drift is represented in terms of the quadration variation of the given local martingale.
Theorem 2.1.11. IfM is a continuous(FtP, P)-local martingale withM0 = 0, then there is a (GtP)-predictable process α such that P-a.s.
Z ∞ 0
α2t d[M, M]t≤[L, L]c∞ <∞,
and
M −α·[M, M] is a (GtP)-local martingale.
Proof.LetM be a continuous (FtP, P)-local martingale withM0 = 0. By the Kunita-Watanabe Inequality one has for 0≤s < t,
[M, L]t−[M, L]s ≤[L, L]1/2t ([M, M]t−[M, M]s)1/2.
Since [L, L]tis finite for allt≥0, the measured[M, L] is absolutely continuous with respect to d[M, M] and there exists a (GtP)-predictable processα with
α·[M, M] = [M, L] = [M, Lc]
(see Lemme 1.36 in [30]). Moreover, the processes M and O = Lc −α·M are orthogonal w.r.t. [·,·]. Consequently,
α2·[M, M] = [α·M, α·M]≤[Lc, Lc] = [L, L]c. Recall that
[L, L] = 1
Z¯−2 ·[ ¯Z,Z¯]
◦ψ
and that ¯Z is a uniformly integrable nonnegative ( ¯FtQ¯,Q)-martingale. Since¯ P¯-a.s. ¯Z∞ > 0, one has also inft≥0Z¯t > 0, ¯P-a.s. Moreover, [ ¯Z,Z]¯∞ < ∞, Q-a.s. Therefore, [ ¯¯ L,L] is ¯¯ P-a.s. bounded and consequently [L, L]ct converges as t→ ∞, P-a.s. to some real value which we denote by [L, L]c∞.