The question: Let $\{ X_{i} \}_{i=1}^{\infty}$ be independent uniformly integrable random variables with common mean $\mu = 0$. Then, $Y_{n} \equiv \sum_{i=1}^{n} X_{i}$ is a martingale wrt $\{ X_{n} \}$.
To show this, the three following characteristics must be shown.
i) That $Y_{n}$ is $\sigma ( X_1, ..., X_n )$-measurable $\forall n$.
ii) $\mathbb{E}[|Y_{n}|] < \infty$
iii) $\mathbb{E}[ Y_{n+1}|\{ X_{i} \}_{i=1}^{n}] = Y_{n}$
I can do (iii) without issue, obviously assuming (i)-(ii) hold. But how do I show (i) and (ii), or rather, what is sufficient?
For (i) for example, $X_{n}$ is $X_{n}$-measurable obviously, then as $Y_{n}$ is a function of only $X_{n}$, is that sufficient to say that $Y_{n}$ is $X_{n}$-measurable?
(ii) I know is a direct result from $X_{n}$ being uniformly integrable. Ie, they are tight, do not diverge to infinity, and tail end does not impact the expectation much. But would it be sufficient to say this?
For (i), if $i \leq n$, then $X_i$ is $\sigma(X_1,...,X_n)$-measurable. Hence, $Y_n$ is the sum of $\sigma(X_1,...,X_n)$-measurable random variables. This implies that $Y_n$ is $\sigma(X_1,...,X_n)$-measurable.
For (ii), uniform integrability of the sequence implies that each $X_n$ is integrable. In fact we have something even stronger, namely
$$\sup_n E|X_n| = \sup_n \Big[ E\big(|X_n|\mathbf{1}_{|X_n > \alpha|} \big) + E\big(|X_n|\mathbf{1}_{|X_n| \leq \alpha}\big) \Big] \leq 1 + \alpha < \infty$$
for suitably large $\alpha$, by uniform integrability. (Note that we did not need to appeal to independence in this argument.)
At any rate, we now have $E|Y_n| \leq \sum_{i=1}^n E|X_n|<\infty$.