Why this Ito integral has zero mean? Let $W_t$ be the one-dimensional Wiener process.
Does the following integral
$$\int_0^t f(W_s) dW_s,$$
where $f:\mathbb{R} \to \mathbb{R}$ continuous with second derivative, have zero mean for any fixed $t>0$?
The integral can be considered as a limit in probability of the Riemann sums $\sum_i f(W_{t_i}) (W_{t_{i+1}} - W_{t_i})$, whose means are zero. But dominated convergence theorem cannot be applied here.
 A: Short answer: The stochastic integral, in Kunita-Watanabe sense, defines a martingale. Read P.195 of the article http://www-math.mit.edu/~dws/ito/ito7.pdf.
Long answer: We need to ask how is the stochastic integral defined. To answer such question, we need a lot of preparation (e.g. a chapter in a textbook). You may consult the famous book "Probabilities and Potential" by Claude Dellacherie and Paul-Andre Meyer.
Although it is true that the Riemann sum converges to the stochastic integral in probability, this fact is very hard to prove and there is a lot of technical difficulty. For example, imagine that we have not defined the stochastic integral yet, then:
How can we tell the Riemann sums converge to some random variable in probability? In what sense? (It is not a sequence of random variables, but involving choices of $t_i$)
A: This is not really rigorous (the other answer covers that) and might be trivial, but I wanted to add this answer from a more intuitive perspective. For me, $\int f(W_s)\,dW_s$ always makes me do a double take, since $dW_s$ is such an odd creature, i.e. not an independent variable in the sense one is used to seeing.
Consider the Brownian increment $\Delta W_{t_{i+1}} = W_{t_{i+1}} - W_{t_i}$. By the definition of a Wiener process, if $\Delta t = t_{i+1}-t_i$, then (1) $\Delta W_{t_{i+1}} \sim \mathcal{N}(0,\Delta t) $ and (2) $W_{t_i}$ is independent from $\Delta W_{t_{i+1}}$.
Thus, expanded as a sort of Ito-Riemann sum:
\begin{align}
\mathbb{E}\left[ \int_0^t f(W_s)\;dW_s \right]
&\approx \mathbb{E}\left[ \sum_i f(W_{t_i})[W_{t_{i+1}} - W_{t_i}] \right] \\
&= \sum_i \mathbb{E}\left[ f(W_{t_i})\Delta W_{t_{i+1}} \right] \\
&= \sum_i \mathbb{E}\left[ f(W_{t_i})\right] \mathbb{E}\left[\Delta W_{t_{i+1}} \right]\;\;\;\;\;\;\;\;\;\;\;\text{ By (2)} \\
&= \sum_i \mathbb{E}\left[ f(W_{t_i})\right] 0 \;\;\;\;\;\;\;\;\;\;\;\;\,\;\;\;\;\;\;\;\;\;\;\;\text{ By (1)} \\
&=0
\end{align}
