Let $(\Omega, \mathscr{F}, P)$ be a probability space and $\mathscr{A}, \mathscr{G}\subset \mathscr{F}$ be $P$-independant sub-$\sigma$-algebras. Let $f$ be $\mathscr{A}$ measurable and $g$ be $\mathscr{G}$ measurable and $f,g\in \mathscr{L}^1(P)$ and $f,g:\Omega \rightarrow \mathbb{R}$.
I want to show that the product $fg\in \mathscr{L}^1(P)$ and that $$\int_\Omega (fg) dP = (\int_\Omega (f) dP)(\int_\Omega (g) dP)$$ This problem is from my measure theory course, not Stochastics.
For the first part I have the following: $$fg=(f_+-f_-)(g_+-g_-)=f_+g_+-f_+g_--f_-g_++f_-g_-\quad (*)$$ Each term on the r.h.s. is the product of measurable functions, ergo the terms are all measurable. Also, the terms are bounded, as $$f\in \mathscr{L}^1(P)\implies\int_\Omega f_\pm dP < \infty\implies f_\pm < \infty$$ for almost all points in $\Omega$. Together with $P(\Omega)=1$ this is sufficient to show that $fg\in \mathscr{L}^1(P)$
For the second part, my idea was to use $(*)$ to define the integral $\int_\Omega (fg) dP$. At any given point, only one of the terms is non-zero. I thought there may be a way to partition $\Omega$ to use this fact and get a nice expression for the integral.
I'm stuck. The terms of $(*)$ are products, I don't know how to work around that when integrating. Also, some of the terms are negative, and I'm not using the fact that $P(\Omega)=1$, which I think may be important. I only used $P(\Omega)<\infty$.
Is my proof for the first part correct? How can we derive the second part?
$f, g \in \mathscr{L}^1(P)$ doesn't in general imply $fg \in \mathscr{L}^1(P)$, for example$$\int_0^1 x^{-\frac{2}{3}}\textrm{d}x = \left[\frac{1}{3}x^{\frac{1}{3}}\right]_0^1 = \frac{1}{3}\textrm{, but }\int_0^1 \left(x^{-\frac{2}{3}}\right)^2\textrm{d}x = \left[-\frac{1}{3}x^{-\frac{1}{3}}\right]_0^1 = \infty,$$so to show this, you have to make use of the fact that $f$ and $g$ are measurable with respect to mutually independent $\sigma$-algebras.
To show that $\int fg = \int f \cdot \int g$, one can start with simple functions, i.e. measurable functions that take only finitely many distinct values. Let $\phi = \sum_i a_i \mathbf{1}_{A_i}$ be $\mathscr{A}$-measurable, and let $\psi = \sum_j b_j \mathbf{1}_{B_j}$ be $\mathscr{G}$-measurable. Then$$\phi\psi = \sum_{i,j}a_i b_j \mathbf{1}_{A_i \cap B_j}$$and thus$$\int\phi\psi\textrm{d}P = \sum_{i,j}a_i b_j P(A_i \cap B_j) = \sum_i a_i P(A_i) \sum_j b_j P(B_j) = \int\phi\textrm{d}P\int\psi\textrm{d}P.$$
The best way to extend this to arbitrary $f$ and $g$ depends on what facts about integrals you have available. If, for example, you know that non-negative measurable functions can be approximated from below by simple functions and you know the Monotone Convergence Theorem, you can simply write$$\int f_+ \textrm{d}P \int g_+ \textrm{d}P = \lim_{n\to\infty}\int\phi_n\textrm{d}P\cdot\lim_{n\to\infty}\int\psi_n\textrm{d}P = \lim_{n\to\infty}\int\phi_n\psi_n\textrm{d}P = \int f_+g_+ \textrm{d}P$$and conclude that, since the left side is finite, so is the right, and therefore $f_+g_+ \in \mathscr{L}^1(P)$.
The general case for possibly negative $f$ and $g$ then follows from your equation $(*)$, since integration is linear.