Correlations are not free of one another: the correlation matrix must be positive semidefinite. Write $\rho=\rho_{23}$ and impose $\det R\ge0$ on

$R=\begin{pmatrix}1&0.6&0.8\\0.6&1&\rho\\0.8&\rho&1\end{pmatrix}.$

Expanding,

$\det R=\bigl(1-\rho^{2}\bigr)-0.6\bigl(0.6-0.8\rho\bigr) +0.8\bigl(0.6\rho-0.8\bigr)$

$=1-\rho^{2}-0.36+0.48\rho+0.48\rho-0.64 =-\rho^{2}+0.96\rho=\rho\,(0.96-\rho).$

Requiring $\rho(0.96-\rho)\ge0$ gives

$0 \le \rho_{23} \le 0.96 .$

The same result follows from the general bound

$\rho_{12}\rho_{13}-\sqrt{\bigl(1-\rho_{12}^{2}\bigr)\bigl(1-\rho_{13}^{2}\bigr)} \le \rho_{23} \le\ \rho_{12}\rho_{13}+\sqrt{\bigl(1-\rho_{12}^{2}\bigr)\bigl(1-\rho_{13}^{2}\bigr)},$

which here reads $0.48\mp\sqrt{0.64\times0.36}=0.48\mp0.48$ , i.e.
$[0,\,0.96]$ .

So $\rho_{23}$ cannot be negative — if $X_2$ and $X_3$ are each strongly positively correlated with $X_1$ , they cannot be negatively correlated with each other. That kills (A) and (C), and (B) is too generous at the top end since $\rho_{23}=1$ is infeasible.