Correlation for a discrete time markov chain Question
Let $(X_n)_{n\in \mathbb{N}}$ be an irreducible Discrete Time Markov Chain (DTMC) with finite state space $S$, transition matrix $P$ and steady state $\pi$. Assume that we are ''far enough'' in time that we may assume that for all $n$ $X_n \overset{d}{=} \pi$ then we define:
$$
\ell_m := \mbox{Corr}(X_{n}, X_{n+m}),
$$
since we are working with a Markov chain one would think that for all $m$, $\ell_m$ can be expressed in function of $\ell_1$. My question is : Is there a good way to see this and what this relation is? i.e. find for each $m$ the function $f_m$ s.t. $\ell_m = f_m(\ell_1)$. As show below, for the special case $|S| = 2$ the function $f_m$ is just given by $f_m(x) := x^m$.
Special Case
In a very special case : a state space with only 2 states we have with $\pi = [\alpha\quad 1-\alpha]$ that for some constant $a \in \left[\max\left(2-\frac{1}{\alpha},0\right),1\right]$:
$$
P = \begin{pmatrix}
a & 1-a\\
\frac{\alpha-\alpha a}{1-\alpha} & \frac{\alpha a - 2\alpha + 1}{1-\alpha}.
\end{pmatrix}
$$
This allows one to see (by trivial calculations) that $\ell_1 = \mbox{Det}(P)$. But then we see, as $\ell_m$ is just $\ell_1$ for the adapted process $Y_n := X_{n\cdot m}$:
$$
\ell_m = \mbox{Det}(P^m) = \mbox{Det}(P)^m = \ell_1^m
$$
 A: Here is a simple counterexample that is a bit too long for a comment. 
Consider two discrete-time Markov chains on $S=\{1,2,3\}$ with the following two transition matrices:
$$
P_1 = \begin{bmatrix} 0 & 1 & 0 \\
0 & 0 & 1 \\
1 & 0 & 0 \end{bmatrix} \;,
\quad 
P_2 = \begin{bmatrix} 0 & 1/2 & 1/2 \\
1/2 & 0 & 1/2 \\
1/2 & 1/2 & 0 \end{bmatrix}
$$
Both chains are irreducible and leave the uniform distribution invariant. The corresponding discrete-time Markov chains have the same lag-$1$ equilibrium autocorrelation, i.e.,  $\ell_1=-1/2$.  However, for $k>1$ their lag-$k$ autocorrelations  are quite different since the first chain is periodic with period $3$ while the second chain's lag-$k$ autocorrelation decays to zero with $k$.
This is not quite a counterexample, since the first chain does not have a steady-state or limiting  distribution.  To correct this deficiency, we break its periodicity by slightly perturbing its entries using a small parameter $\epsilon>0$:
$$
\tilde P_1 = \begin{bmatrix} 0 & 1-\epsilon & \epsilon \\
\epsilon & 0 & 1-\epsilon \\
1-\epsilon & \epsilon & 0 \end{bmatrix}
$$ The lag-$k$ correlation functions for the chains with transition matrices $\tilde P_1$ (blue line) and $P_2$ (black line) are plotted below with $\epsilon=1/25$ (chosen for visualization purposes only).  The inset shows the first few lag correlations.  Note that $\ell_1$ is the same for the two chains.  

ADD
As the OP (HolyMonk) points out in the comments to this answer: for $\tilde P_1$ (given above) and for any $\epsilon>0$, we have $\ell_1 = -1/2$ and $\ell_2 = -1/2−3(−1+ϵ)ϵ$.
