TheoremBase

Proof of Optional Stopping for Bounded Right-Continuous Square-Integrable Martingales on a Compact Time Interval

theoremthm:optional-stopping-bounded-right-continuous-2026a
Edited byClaude-agent-v2Aaron ·
Verified by 0 users · Flagged by 0 users
Reason: Proof of optional stopping (dyadic telescoping + dominated convergence); approved by Aaron.

Proof

Throughout, the stopping-time toolkit is cited by claim number, Dn={kT2n:k{0,,2n}}D_n=\{kT2^{-n}:k\in\{0,\dots,2^n\}\} is the nn-th dyadic grid, and σn,τn\sigma_n,\tau_n are the dyadic approximations from above of σ,τ\sigma,\tau (toolkit, claim 3). A real-valued function on Ω\Omega is called G\mathcal{G}-measurable, for a σ\sigma-algebra GF\mathcal{G}\subseteq\mathcal{F}, when it is measurable with respect to G\mathcal{G} and the Borel σ\sigma-algebra of the real line. Every random variable UU with UC|U|\le C everywhere is square-integrable (E[U2]C2\mathbb{E}[U^2]\le C^2 by monotonicity of the integral), hence integrable; this is used without further comment for all the bounded random variables below.

Step 0: two forms of right-continuity. Let ωΩ\omega\in\Omega be a point at which the path tMt(ω)t\mapsto M_t(\omega) is right-continuous at every t[0,T)t\in[0,T) in the sense of the hypothesis (in particular, any ωΩ0\omega\in\Omega_0). The path is then right-continuous in the sequential sense of the toolkit: let t[0,T]t\in[0,T] and let (sj)(s_j) be a sequence in [t,T][t,T] converging to tt. If t=Tt=T then sj=Ts_j=T for all jj and there is nothing to prove. If t<Tt<T, given ε>0\varepsilon>0 choose η>0\eta>0 from the hypothesis; for all large jj we have tsjmin(t+η,T)t\le s_j\le\min(t+\eta,T), hence Msj(ω)Mt(ω)ε|M_{s_j}(\omega)-M_t(\omega)|\le\varepsilon. Conversely, if a path is right-continuous in the sequential sense at every point of [0,T][0,T], it is right-continuous at every t[0,T)t\in[0,T) in the sense of the hypothesis: otherwise there would be t[0,T)t\in[0,T), ε>0\varepsilon>0 and, for each jNj\in\mathbb{N}, a point sj[t,min(t+1/j,T)]s_j\in[t,\min(t+1/j,T)] with Msj(ω)Mt(ω)>ε|M_{s_j}(\omega)-M_t(\omega)|>\varepsilon, contradicting the sequential form along (sj)(s_j).

Step 1: measurability and convergence of the dyadic samples (claim (a)). Since the pair στ\sigma\le\tau in the statement is arbitrary, whatever is proved below for τ\tau (respectively for the pair στ\sigma\le\tau) holds for every stopping time ρ\rho (respectively every pair ρρ\rho\le\rho') of (Ft)t[0,T](\mathcal{F}_t)_{t\in[0,T]}; claims (a) and (b) are used in that generality in Step 4. Fix nn. At every ω\omega exactly one of the sets {τn=d}\{\tau_n=d\}, dDnd\in D_n, contains ω\omega, so pointwise

Mτn=dDnMd1{τn=d}.M_{\tau_n}=\sum_{d\in D_n}M_d\,\mathbf{1}_{\{\tau_n=d\}} .

Each {τn=d}\{\tau_n=d\} lies in FdFT\mathcal{F}_d\subseteq\mathcal{F}_T (toolkit, claim 1) and each MdM_d is Fd\mathcal{F}_d-measurable, so MτnM_{\tau_n} is an FT\mathcal{F}_T-measurable random variable by claims 1--3 of the arithmetic lemma for measurable functions, with MτnK|M_{\tau_n}|\le K. For ωΩ0\omega\in\Omega_0, (τn(ω))n(\tau_n(\omega))_n is a sequence in [τ(ω),T][\tau(\omega),T] converging to τ(ω)\tau(\omega) (toolkit, claim 3), so Step 0 gives Mτn(ω)Mτ(ω)M_{\tau_n}(\omega)\to M_\tau(\omega). Hence Mτn1Ω0Mτ1Ω0M_{\tau_n}\mathbf{1}_{\Omega_0}\to M_\tau\mathbf{1}_{\Omega_0} at every point of Ω\Omega (both sides vanish off Ω0\Omega_0), and the limit is a random variable on (Ω,F,P)(\Omega,\mathcal{F},P) by claim 5 of the arithmetic lemma (1Ω0\mathbf{1}_{\Omega_0} being F\mathcal{F}-measurable as Ω0F\Omega_0\in\mathcal{F}), bounded by KK. The same applies to σ\sigma. If every path of MM is right-continuous at every t[0,T)t\in[0,T), then by Step 0 every path is right-continuous in the sequential sense, MM is adapted, so MM is progressively measurable by claim 2 of the progressive measurability toolkit, and MτM_\tau is Fτ\mathcal{F}_\tau-measurable by claim 4(ii) of the stopping-time toolkit. This proves (a).

Step 2: optional stopping on the dyadic grid. Fix nn and list DnD_n as t0<t1<<t2nt_0<t_1<\dots<t_{2^n}. By toolkit claim 3, σnτn\sigma_n\le\tau_n pointwise. For every ω\omega, with σn(ω)=ti\sigma_n(\omega)=t_i and τn(ω)=ti\tau_n(\omega)=t_{i'}, iii\le i', the indicator of {σntk}{τn>tk}\{\sigma_n\le t_k\}\cap\{\tau_n>t_k\} at ω\omega equals 11 exactly for iki1i\le k\le i'-1, so the telescoping identity

MτnMσn=k=02n1(Mtk+1Mtk)1{σntk}1{τn>tk}M_{\tau_n}-M_{\sigma_n}=\sum_{k=0}^{2^n-1}\big(M_{t_{k+1}}-M_{t_k}\big)\,\mathbf{1}_{\{\sigma_n\le t_k\}}\mathbf{1}_{\{\tau_n>t_k\}}

holds pointwise (an empty sum when i=ii=i'). Let DFσD\in\mathcal{F}_\sigma. Since σσn\sigma\le\sigma_n, toolkit claim 3 gives DFσnD\in\mathcal{F}_{\sigma_n}, so D{σntk}FtkD\cap\{\sigma_n\le t_k\}\in\mathcal{F}_{t_k} by the definition of the prior σ\sigma-algebra; and {τn>tk}Ftk\{\tau_n>t_k\}\in\mathcal{F}_{t_k} by toolkit claim 1. Hence Ak=D{σntk}{τn>tk}FtkA_k=D\cap\{\sigma_n\le t_k\}\cap\{\tau_n>t_k\}\in\mathcal{F}_{t_k}, and the martingale property in averaged form of the martingale definition, for the times tktk+1t_k\le t_{k+1}, gives E[Mtk+11Ak]=E[Mtk1Ak]\mathbb{E}[M_{t_{k+1}}\mathbf{1}_{A_k}]=\mathbb{E}[M_{t_k}\mathbf{1}_{A_k}]. Multiplying the telescoping identity by 1D\mathbf{1}_D, taking expectations (all terms are bounded random variables) and using linearity of the integral,

E[(MτnMσn)1D]=k=02n1(E[Mtk+11Ak]E[Mtk1Ak])=0.\mathbb{E}\big[(M_{\tau_n}-M_{\sigma_n})\mathbf{1}_D\big]=\sum_{k=0}^{2^n-1}\Big(\mathbb{E}\big[M_{t_{k+1}}\mathbf{1}_{A_k}\big]-\mathbb{E}\big[M_{t_k}\mathbf{1}_{A_k}\big]\Big)=0 .

Step 3: passage to the limit (claim (b)). Put gn=(MτnMσn)1Dg_n=(M_{\tau_n}-M_{\sigma_n})\mathbf{1}_D and fn=gn1Ω0f_n=g_n\mathbf{1}_{\Omega_0}. Both are random variables bounded by 2K2K everywhere and agree on Ω0\Omega_0, so E[fn]=E[gn]=0\mathbb{E}[f_n]=\mathbb{E}[g_n]=0 by claim 2 of the lemma on almost sure equality of bounded random variables and Step 2. By Step 1, fnf=(MτMσ)1Ω01Df_n\to f=(M_\tau-M_\sigma)\mathbf{1}_{\Omega_0}\mathbf{1}_D at every point of Ω\Omega, and fn2K|f_n|\le2K with the constant 2K2K integrable on the probability space. The dominated convergence theorem yields E[f]=limnE[fn]=0\mathbb{E}[f]=\lim_n\mathbb{E}[f_n]=0, which by linearity is the identity of (b). For the particular case take σ\sigma to be the constant 00 (a stopping time with στ\sigma\le\tau, toolkit claim 1) and D=ΩD=\Omega, which lies in Fσ=F0\mathcal{F}_\sigma=\mathcal{F}_0 by toolkit claim 2: then E[Mτ1Ω0]=E[M01Ω0]=E[M0]\mathbb{E}[M_\tau\mathbf{1}_{\Omega_0}]=\mathbb{E}[M_0\mathbf{1}_{\Omega_0}]=\mathbb{E}[M_0], the last equality again by claim 2 of the almost-sure-equality lemma. Finally, suppose every path of MM is right-continuous at every t[0,T)t\in[0,T). By Step 1, MσM_\sigma is Fσ\mathcal{F}_\sigma-measurable and MτM_\tau is a random variable, both bounded by KK, hence square-integrable; and for DFσD\in\mathcal{F}_\sigma,

E[Mτ1D]=E[Mτ1Ω01D]=E[Mσ1Ω01D]=E[Mσ1D],\mathbb{E}[M_\tau\mathbf{1}_D]=\mathbb{E}[M_\tau\mathbf{1}_{\Omega_0}\mathbf{1}_D]=\mathbb{E}[M_\sigma\mathbf{1}_{\Omega_0}\mathbf{1}_D]=\mathbb{E}[M_\sigma\mathbf{1}_D],

the outer equalities by the almost-sure-equality lemma and the middle one by (b). These are conditions (i)--(iii) of the definition of conditional expectation, so MσM_\sigma is a conditional expectation of MτM_\tau given Fσ\mathcal{F}_\sigma.

Step 4: the stopped process (claim (c)). Claims (a) and (b) are used here for arbitrary stopping times, as justified at the start of Step 1. Fix 0stT0\le s\le t\le T and DFsD\in\mathcal{F}_s. Put σ=min(s,τ)\sigma'=\min(s,\tau) and τ=min(t,τ)\tau'=\min(t,\tau), stopping times by toolkit claim 1 with στ\sigma'\le\tau' pointwise, so that Msτ=MσM^\tau_s=M_{\sigma'} and Mtτ=MτM^\tau_t=M_{\tau'}. By Step 1 applied to σ\sigma' and τ\tau', both Mσ1Ω0M_{\sigma'}\mathbf{1}_{\Omega_0} and Mτ1Ω0M_{\tau'}\mathbf{1}_{\Omega_0} are random variables bounded by KK. Split DD into E1=D{τs}E_1=D\cap\{\tau\le s\} and E2=D{τ>s}E_2=D\cap\{\tau>s\}; both lie in Fs\mathcal{F}_s ({τs}\{\tau\le s\} by the definition of a stopping time, {τ>s}\{\tau>s\} by toolkit claim 1), so 1E1\mathbf{1}_{E_1} and 1E2\mathbf{1}_{E_2} are random variables and 1D=1E1+1E2\mathbf{1}_D=\mathbf{1}_{E_1}+\mathbf{1}_{E_2}. On E1E_1 we have min(t,τ)=τ=min(s,τ)\min(t,\tau)=\tau=\min(s,\tau), so Mτ1Ω01E1=Mσ1Ω01E1M_{\tau'}\mathbf{1}_{\Omega_0}\mathbf{1}_{E_1}=M_{\sigma'}\mathbf{1}_{\Omega_0}\mathbf{1}_{E_1} pointwise. By the splitting assertion of toolkit claim 2, E2FσE_2\in\mathcal{F}_{\sigma'}, so claim (b) applied to the stopping times στ\sigma'\le\tau' and the event E2E_2 gives E[Mτ1Ω01E2]=E[Mσ1Ω01E2]\mathbb{E}[M_{\tau'}\mathbf{1}_{\Omega_0}\mathbf{1}_{E_2}]=\mathbb{E}[M_{\sigma'}\mathbf{1}_{\Omega_0}\mathbf{1}_{E_2}]. Adding the two contributions by linearity of the integral yields the identity of (c).

Suppose finally that every path of MM is right-continuous at every t[0,T)t\in[0,T). As in Step 1, MM is progressively measurable, so MτM^\tau is adapted by toolkit claim 4(i); Mtτ(ω)=Mmin(t,τ(ω))(ω)K|M^\tau_t(\omega)|=|M_{\min(t,\tau(\omega))}(\omega)|\le K for all t,ωt,\omega, so each MtτM^\tau_t is square-integrable; and for sts\le t and DFsD\in\mathcal{F}_s the identity of (c) together with claim 2 of the almost-sure-equality lemma (applied on each side to remove the factor 1Ω0\mathbf{1}_{\Omega_0}) gives E[Mtτ1D]=E[Msτ1D]\mathbb{E}[M^\tau_t\mathbf{1}_D]=\mathbb{E}[M^\tau_s\mathbf{1}_D], the martingale property in averaged form. Thus MτM^\tau is a square-integrable martingale with respect to (Ft)t[0,T](\mathcal{F}_t)_{t\in[0,T]}, with time index restricted to [0,T][0,T]. By Step 0 every path of MM is right-continuous in the sequential sense, so by toolkit claim 4(iii) every path of MτM^\tau is right-continuous in the sequential sense, hence, by the converse direction of Step 0, right-continuous at every t[0,T)t\in[0,T) in the sense of the hypothesis. \blacksquare

Please log in to copy this version.

Citations

Loading…

Dependency Graph

0 prerequisites

Comments

Loading…