Let $(\Omega,\mathcal F,\mathbb P)$ be a probability space, and let $X,Y:(\Omega,\mathcal F)\to(\mathbb R,\mathcal B(\mathbb R))$ be real-valued random variables such that $\mathbb E[X^2]<\infty$ and $\mathbb E[Y^2]<\infty$. Define
\begin{align*}
\operatorname{Cov}(X,Y)&:=\mathbb E\!\left[(X-\mathbb E[X])(Y-\mathbb E[Y])\right],\\
\operatorname{Var}(X)&:=\mathbb E\!\left[(X-\mathbb E[X])^2\right],\qquad
\operatorname{Var}(Y):=\mathbb E\!\left[(Y-\mathbb E[Y])^2\right],\\
\sigma_X&:=\sqrt{\operatorname{Var}(X)},\qquad
\sigma_Y:=\sqrt{\operatorname{Var}(Y)}.
\end{align*}
Then
\begin{align*}
|\operatorname{Cov}(X,Y)|\le \sigma_X\sigma_Y.
\end{align*}
If $\operatorname{Var}(X)>0$ and $\operatorname{Var}(Y)>0$, and if
\begin{align*}
\rho(X,Y):=\frac{\operatorname{Cov}(X,Y)}{\sigma_X\sigma_Y},
\end{align*}…