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A càdlàg martingale is a continuous-time martingale represented by a càdlàg process: its paths are right-continuous and have finite left limits at positive times, on one common probability-one event. This path regularity permits approximation of stopping times from above.

An integrable adapted process is a martingale if all its values at bounded stopping times are integrable with the same expectation as $X_0$. For the converse, compare deterministic $t$ with $U=s{1}_A+t{1}_{A^c}$, where $s<t$ and $A\in {\mathcal{F}}_s$. The resulting identity $\mathbb{E}[(X_t-X_s)1_A]=0$ is precisely the defining test for conditional expectation. In the forward direction, use the optional stopping theorem for a càdlàg martingale under the usual conditions for a filtration.

Write the residual process asFor $s<t<1$, the filtration tells us whether each $U_i$ has arrived by time $s$, and its exact arrival time if it has. On $\{U_i>s\}$, its conditional law is uniform on $(s,1)$. Independence of the original $U_i$ means that the histories of the other arrivals do not alter this law. HenceThis conditional identity can also be checked against products of bounded functions of the censored arrival times and extended to ${\mathcal{F}}_s$ by the Monotone class theorem. Summing the identity gives $\mathbb{E}[M_t\mid {\mathcal{F}}_s]=M_s$.

Each $M_t$ is adapted and integrable, withIts paths are smooth between the finitely many arrival times and have downward jumps of size ${\theta }_i/(1-U_i)$ at arrivals before time one. The indicators use $U_i>t$, so the value at a jump is the post-jump value: paths are right continuous with finite left limits on $[0,1)$. Thus $M$ is a càdlàg martingale, specifically the uniform-arrival residual mass martingale.

A càdlàg martingale is an adapted process $X$ with càdlàg paths, integrable $X_t$ at each finite time, andThe càdlàg requirement means that the chosen path version is right-continuous and has a finite left limit at every positive time. With a complete filtration, it suffices that this holds on one common event of probability one, since the process can be redefined on the exceptional null set.

The usual hypotheses for a filtration are completeness and right continuity. Completeness means that ${\mathcal{F}}_0$ contains every subset of every null event of the ambient probability space; right continuity means