I am currently working on this problem that asked me to calculate the ACF of
$Y_{t}=x_{t}x_{t-1}, x \sim IID{\left\lbrace0, \sigma^{2}\right\rbrace}$
what I've done so far is, I have proved that $E[y_{t}]=0$, so I'll take it as given in the following calculation.
here I am calculating the ACF of $y_{t}$: $$ \begin{aligned} COV(y_{t+h}, y_{t}) &= E[(y_{t+h} - E[y_{t+h}])(y_{t} - E[y_{t})]\\ &= E[y_{t+h}y_{t}]\\ &= E[x_{t+h}x_{t+h-1}x_{t}x_{t-1}]\\ \end{aligned} $$
given $x \sim IID{\left\lbrace0, \sigma^{2}\right\rbrace}$, therefore we can separate the expectation above into: $$ E[x_{t+h}x_{t+h-1}x_{t}x_{t-1}] = E[x_{t+h}]E[x_{t+h-1}]E[x_{t}]E[x_{t-1}] \qquad(1) $$
if $h \neq 0$, then $(1)=0$ because we have $E[x_{t}]=0$
if $h = 0$, then (1) is
$$ E[x_{t}]E[x_{t-1}]E[x_{t}]E[x_{t-1}] = 0 $$
which we could then conclude that ACF of $y_{t}$ is 0.
it all looks correct me but the solution gives different answer when $h =0$, could anyone spot what I've done wrong?
thanks!
EDIT:
given the comments, there's one thing I've done wrong above; when $h = 0$, the independence cannot be applied because two or more indices are the same. this implies dependence (I think)
I therefore did,
$$ \begin{aligned} E[x_{t+h}x_{t+h-1}x_{t}x_{t-1}] &= E[x_{t}x_{t-1}x_{t}x_{t-1}]\\ &= E[x_{t}^{2}x_{t-1}^{2}] \qquad (2) \end{aligned} $$
I am stuck again because I don't know how to prove $$ (2) = E[x_{t}^{2}]E[x_{t-1}^{2}]\qquad (3) $$
one more hint, I could solve it!