Step 1: extension of the martingale to all nonnegative times. Define, for t≥0,
Ftext=Fmin(t,T),Mtext=Mmin(t,T).
The family (Ftext)t≥0 is a filtration, since r≤t implies min(r,T)≤min(t,T) and hence Frext⊆Ftext. Each Mtext is measurable with respect to Ftext and square-integrable, being one of the Ms with s∈[0,T]. For 0≤r≤t the conditional expectation of Mtext given Frext equals Mrext: if t≤T this is the martingale property of M on [0,T]; if r≤T≤t it is the martingale property of M for the pair r≤T, because Mtext=MT and Frext=Fr; and if T≤r both sides equal MT, which is measurable with respect to FT=Frext. Hence Mext is a square-integrable martingale indexed by t≥0.
Step 2: the inequality on finite dyadic grids. For a natural number n let Dn={kT2−n:k∈{0,1,…,2n}}, whose elements, listed in increasing order, form a finite family of sample times 0=t0<t1<⋯<t2n=T. Applying Doob's L2 maximal inequality to the square-integrable martingale Mext and these sample times gives that maxt∈Dn∣Mt∣ is square-integrable with
E[t∈Dnmax∣Mt∣2]≤4E[MT2].
The maximum of finitely many random variables is a random variable, since {max(U,V)>c}={U>c}∪{V>c} and one may iterate.
Step 3: passage to the supremum. The sets Dn increase with n and their union is D, so the random variables
Wn=t∈Dnmax(∣Mt∣1Ω0)2
are nondecreasing in n, and Wn≤maxt∈Dn∣Mt∣2, so E[Wn]≤4E[MT2] by Step 2. They converge pointwise to M2: indeed Wn≤M2 for every n, while for ε>0 there is, by the definition of the supremum, some t∈D with ∣Mt∣1Ω0>M−ε when M>0, and this t lies in Dn for all large n; the case M=0 is immediate. By the monotone convergence theorem applied to the nondecreasing nonnegative sequence (Wn), together with the bound E[Wn]≤4E[MT2] just obtained,
E[M2]=n→∞limE[Wn]≤4E[MT2].
Here M is a random variable by the supremum lemma, whose hypotheses hold by assumption. That lemma also gives M(ω)=supt∈[0,T]∣Mt(ω)∣ for every ω∈Ω0, and since P(Ω0)=1 the expectation of the pathwise supremum, computed on Ω0, coincides with E[M2]. ■