Time change of continuous martingales (Dambis-Dubins-Schwarz)

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Let {Ft}\left\{ \mathcal{F}_{t}\right\} be a filtration satisfying the usual conditions. Let AtA_{t} be a strictly increasing continuous process adapted to {Ft}\left\{ \mathcal{F}_{t}\right\} with lim⁡t→∞At=∞\lim_{t \rightarrow \infty } A_{t}=\infty, a.s. Suppose (Mt1,…,Mtd)\left(M_{t}^{1}, \ldots , M_{t}^{d}\right) is a dd-dimensional process such that each component MtiM_{t}^{i} is a continuous martingale with respect to {Ft}\left\{ \mathcal{F}_{t}\right\} and ⟨Mi⟩t=At\left\langle M^{i}\right\rangle_{t}=A_{t}. Suppose that ⟨Mi,Mj⟩t=0\left\langle M^{i}, M^{j}\right\rangle_{t}=0 if i≠ji \neq j. Prove that (Mt1,…,Mtd)\left(M_{t}^{1}, \ldots , M_{t}^{d}\right) is a time change of dd-dimensional Brownian motion.

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Suppose MM is a continuous local martingale such that ⟨M⟩t\langle M\rangle_{t} is deterministic. Prove that MM is a Gaussian process.

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Suppose MM is a continuous local martingale with M0=0M_{0}=0, a.s. Show that there exists a Brownian motion WW, an increasing process τt\tau_{t}, and a stopping time TT such that Mt=Wτι∧TM_{t}=W_{\tau_{\iota } \wedge T} for all tt.

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Let MtM_{t} be a continuous local martingale. Show that the events (M∞∗<∞)\left(M_{\infty }^{*}<\infty \right) and (⟨M⟩∞<∞)\left(\langle M\rangle_{\infty }<\infty \right) differ by at most a null set.

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Let MtM_{t} be a continuous local martingale. Prove that

P(sup⁡t≥0∣Mt∣>x,⟨M⟩∞<y)≤2e−x2/2y \mathbb {P}\left(\sup _{t \geq 0}\left|M_{t}\right|>x,\langle M\rangle _{\infty }<y\right) \leq 2 e^{-x^{2} / 2 y}
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Let (It,Ft)t⩾0\left(I_{t}, \mathscr {F}_{t}\right)_{t \geqslant 0} be a continuous adapted process with values in [0,∞)[0, \infty ) and a.s. increasing sample paths. Set for u⩾0u \geqslant 0

σu(ω):=inf⁡{t⩾0:It(ω)>u} and τu(ω):=inf⁡{t⩾0:It(ω)⩾u} \sigma _{u}(\omega ): = \inf \left\{ t \geqslant 0: I_{t}(\omega ) > u\right\} \quad \text{ and } \quad \tau _{u}(\omega ): = \inf \left\{ t \geqslant 0: I_{t}(\omega ) \geqslant u\right\}

Show that

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(a)

σu⩾t⟺It⩽u\sigma_{u} \geqslant t \Longleftrightarrow I_{t} \leqslant u and τu>t⟺It<u\tau_{u} > t \Longleftrightarrow I_{t} < u.

(b)

τu\tau_{u} is an Ft\mathscr {F}_{t} stopping time and σu\sigma_{u} is an Ft+\mathscr {F}_{t+} stopping time.

(c)

u↦σuu \mapsto \sigma_{u} is right continuous and u↦τuu \mapsto \tau_{u} is left continuous.

(d)

τu=σu−=lim⁡ϵ↓0σu−ϵ\tau_{u} = \sigma_{u - } = \lim_{\epsilon \downarrow 0} \sigma_{u - \epsilon }.

(e)

u↦τu,u↦σuu \mapsto \tau_{u}, u \mapsto \sigma_{u} are continuous, if t↦Itt \mapsto I_{t} is strictly increasing.

Show the following addition to Lemma 19.16:

τ(t−)=inf⁡{s⩾0:a(s)⩾t} and a(t−)=inf⁡{s⩾0:τ(s)⩾t} \tau (t - ) = \inf \left\{ s \geqslant 0: a(s) \geqslant t\right\} \quad \text{ and } \quad a(t - ) = \inf \left\{ s \geqslant 0: \tau (s) \geqslant t\right\}

and τ(s)⩾t⟺a(t−)⩽s\tau (s) \geqslant t \Longleftrightarrow a(t - ) \leqslant s.

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Show that, in Theorem 19.17, the quadratic variation ⟨M⟩t\left\langle M\right\rangle_{t} is a Gt\mathscr {G}_{t} stopping time.

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References

[Bas11] Bass R.F. Stochastic Processes. Cambridge University Press, 2011.
[Sch21] Schilling R.L. Brownian Motion: A Guide to Random Processes and Stochastic Calculus. 3rd ed., De Gruyter, 2021.