Let $(\Omega,\mathcal A,\operatorname P)$ be a probability space, $(\mathcal F_t)_{t\ge0}$ be a filtration on $(\Omega,\mathcal A,\operatorname P)$ and $(N_t)_{t\ge0}$ be a $\mathcal F$-Poisson process on $(\Omega,\mathcal A,\operatorname P)$ with intensity $\lambda>0$, i.e.
- $N$ is an $\mathbb N_0$-valued $\mathcal F$-adapted process on $(\Omega,\mathcal A,\operatorname P)$;
- $N_0=0$;
- $N_t-N_s$ and $\mathcal F_s$ are independent for all $t\ge s\ge0$;
- $N_t-N_s$ is Poisson distributed with parameter $\lambda(t-s)$ for all $t\ge s\ge0$.
Assuming $N$ is almost surely right-continuous, I was able to show that
- $N$ is almost surely nondecreasing
and assuming that $N$ is almost surely càdlàg, I was able to show that$^1$
- $\operatorname P\left[\forall t\ge0:\Delta N_t\in\{0,1\}\right]=1$.
Assume $N$ is (surely) càdlàg. Let $\tau_0:=0$ and $$\tau_k:=\inf\left\{t>\tau_{k-1}:\Delta N_t\ne0\right\}$$ for $k\in\mathbb N$.
Are we able to show that
- $\operatorname P\left[\forall n\in\mathbb N_0:\exists t\ge0:N_t=n\right]=1$;
- $\tau_k$ is a $\mathcal F$-stopping time for all $k\in\mathbb N$;
- $\tau_k$ is almost surely finite for all $k\in\mathbb N$.
- $N_t=\sum_{k\in\mathbb N_0}1_{\left\{\:\tau_k\:\le\:t\:\right\}}$ for all $t\ge0$ almost surely.
All these claims are intuitively trivial, but I really worry about some of the technical details we need to prove them rigorously. For example, does (8.) really hold or do we need to replace $\mathcal F$ with the right-continuous filtration generated by it?
For (7.), I've tried to consider $$\operatorname P\left[N_t<n\right]=\sum_{k=0}^{n-1}\operatorname P\left[N_t=k\right]=e^{-\lambda t}\sum_{k=0}^{n-1}\frac{(\lambda t)^k}{k!}\tag{11}$$ for all $t\ge0$ and $n\in\mathbb N$. $(11)$ would tend to $0$ as $t\to\infty$ if $t^ke^{-\lambda t}\xrightarrow{t\to\infty}0$, but does this really hold? We clearly got $t^ke^{-\lambda t}=e^{k\ln t-\lambda t}$, but now the only useful inequality I'm aware of is $\ln t\le t-1$ for all $t>0$, which is not enough to conclude $k\ln t-\lambda t\xrightarrow{t\to\infty}-\infty$.
$^1$ As usual, if $x:[0,\infty)\to\mathbb R$ is càdlàg, then $x(t-):=\lim_{s\to t-}x(s)$ and $\Delta x(t):=x(t)-x(t-)$ for $t\ge0$.
$\{\tau_1\le t\} = \{N_t\ge 1\}\in\mathcal F_t$. Likewise for subsequent $\tau_k$.
${\rm P}[\tau_1<\infty] = \lim_{t\to\infty}{\rm P}[\tau_1\le t]=\lim_{t\to\infty}{\rm P}[N_t\ge 1]=\lim_{t\to\infty}1-e^{-\lambda t}=1$. Similarly for subsequent $\tau_k$.
This should read $N_t = \sum_{k\ge 1}1_{\{\tau_k\le t\}}$. Both sides of the equality-to-be are non-decreasing, cadlag, with jumps of size 1, and initial values $0$. To see that they agree you can check that they have the same jumps at each time $t$. But this is clear because $\Delta N_t=1$ if and only if $t=\tau_k$ for some $k\ge 1$.
This follows from 6. and 8.