I was reading Oksendal's book which is Stochastic Differential Equations An Introduction with Applications. In Chapter 3, he introduces the concept of SDE by adding noise term to the coefficient. We can think noise term as a stochastic process such that:
$$\frac{dX_t}{dt}=b(t, X_t)+\sigma(t, X_t) W_t$$
where $W_t$ is any stochastic process (not white noise for now).
But for me, things are getting complicated here. He says;
Based on many situations, for example in engineering, one is led to assume that $W_t$ has, at least approximately, these properties:
(i) for $t_1 \neq t_2$, $W_{t_1}$ and $W_{t_2}$ are independent.
(ii) $W_t$ is stationary, i.e. the (joint) distribution of $\{W_{{t_1}+t},...,W_{{t_k}+t}\}$ does not depend on $t$.
(iii) ${\mathbb{E}}\left[W_t\right]=0$ for all $t$.
Here is my question: Why are we assuming that stochastic process $W_t$ has to satisfy these properties? Is this the only way to define SDEs or is there any other way? I also upload an image about related part.
Thanks in advance.
