Yule's "nonsense correlation" solved: Part II

In 1926, G. Udny Yule,considered the following: given a sequence of pairs of random variables $\{X_k,Y_k \}$ ($k=1,2, \ldots, n$), and letting $X_i = S_i$ and $Y_ i= S'_i$ where $S_i$ and $S'_i$ are the partial sums of two independent random walks, what is the distribution of the empirical correlation coefficient \begin{equation*} \rho_n = \frac{\sum_{i=1}^n S_i S^\prime_i - \frac{1}{n}(\sum_{i=1}^n S_i)(\sum_{i=1}^n S^\prime_i)}{\sqrt{\sum_{i=1}^n S^2_i - \frac{1}{n}(\sum_{i=1}^n S_i)^2}\sqrt{\sum_{i=1}^n (S^\prime_i)^2 - \frac{1}{n}(\sum_{i=1}^n S^\prime_i)^2}}? \end{equation*} Yule empirically observed the distribution of this statistic to be heavily dispersed and frequently large in absolute value, leading him to call it "nonsense correlation." This unexpected finding led to his formulation of two concrete questions, each of which would remain open for more than ninety years: (i) Find (analytically) the variance of $\rho_n$ as $n \rightarrow \infty$ and (ii): Find (analytically) the higher order moments and the density of $\rho_n$ as $n \rightarrow \infty$. In 2017, \cite{ernst2017yule} considered the empirical correlation coefficient \begin{equation*} \rho:= \frac{\int_0^1W_1(t)W_2(t) dt - \int_0^1W_1(t) dt \int_0^1 W_2(t) dt}{\sqrt{\int_0^1 W^2_1(t) dt - \parens{\int_0^1W_1(t) dt}^2} \sqrt{\int_0^1 W^2_2(t) dt - \parens{\int_0^1W_2(t) dt}^2}} \end{equation*} of two \textit{independent} Wiener processes $W_1,W_2$, the limit to which $\rho_n$ converges weakly, as was first shown by the author of \cite{phil}. Using tools from integral equation theory, \cite{ernst2017yule} closed question (i) by explicitly calculating the second moment of $\rho$ to be .240522. This paper begins where \cite{ernst2017yule} leaves off. We succeed in closing question (ii) by explicitly calculating all moments of $\rho$ (up to order 16).