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*} ρ_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 $ρ_n$ as $n \rightarrow \infty$ and (ii): Find (analytically) the higher order moments and the density of $ρ_n$ as $n \rightarrow \infty$. In 2017, Ernst, Shepp, and Wyner considered the empirical correlation coefficient \begin{equation*} ρ:= \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 - (\int_0^1W_1(t) dt)^2} \sqrt{\int_0^1 W^2_2(t) dt - (\int_0^1W_2(t) dt)^2}}\end{equation*} of two independent Wiener processes $W_1,W_2$, the limit to which $ρ_n$ converges weakly, as was first shown by Phillips (1986). Using tools from integral equation theory, Ernst et al. (2017) closed question (i) by explicitly calculating the second moment of $ρ$ to be .240522. This paper begins where Ernst et al. (2017) leaves off. We succeed in closing question (ii) by explicitly calculating all moments of $ρ$ (up to order 16).